Development of a computer aided diagnosis software for the evaluation of lung nodules in computed tomography scans

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DEVELOPMENT OF A COMPUTER AIDED DIAGNOSIS SOFTWARE

FOR THE EVALUATION OF LUNG NODULES IN COMPUTED

TOMOGRAPHY SCANS

By

Juliana De La Vega Fern´

andez

A dissertation submitted in partial satisfaction of the requirements for the degree of

BACHELOR OF ENGINEERING

in

BIOMEDICAL ENGINEERING

in the

BIOMEDICAL ENGINEERING DEPARTMENT

of the

UNIVERSIDAD DE LOS ANDES

Committee in charge:

- Advisor: Pablo Andr´es Arbel´aez Escalante PhD, Professor, Universidad de Los Andes

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Abstract

Lung cancer is one of the types of cancer with the highest mortality worldwide. Early detection of this type of cancer could significantly improve the survival rate of its victims. Within this dissertation, a computer aided diagnosis software for the detection of lung nodules is presented. The system uses diverse computer vision techniques in order to extract candidate nodules and represent them. A Support Vector Machine is implemented in order to classify these candidates. 1006 CT images from the LIDC-IDRI database is used in order to train, validate and test the algorithm. The algorithm proposed evaluated in the validation set shows up to 96.75% recall, and 5.556% precision, and 97.78% recall, and 0.021% precision in the test set.

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Resumen

El cáncer de pulmón es uno de los tipo de cáncer con la mayor tasa de mortalidad a nivel mundial. La detección temprana de este tipo de cáncer podr´ıa mejorar la supervivencia de estos pacientes de manera considerable. En este documento de tesis se presenta un software de diagnóstico asistido por computador para la detección de nódulos pulmonares. El sistema utiliza diversas técnicas de la visión por computador con el fin de extraer los candidatos y representarlos. Posteriormente se emplea una máquina de soporte de vectores para clasificar dichos candidatos. Para el desarrollo del algoritmo se emplean 1006 tomograf´ıas de tórax de la base de datos LIDC-IDRI. La evaluación del algoritmo sobre la base de datos de validación demostró una cobertura del 96.75%, y una precisión del 5.556%, mientras que en la base de prueba se obtuvo 97.78% de cobertura y 0.021% de precisión.

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List of Figures

1.1 Algorithm overview figure. . . 2

3.1 Data CT image viewed as voxels, or as pixels when analysing a CT slice [1]. . . 8

5.1 Step-by-step schematic for the methodology followed in order to develop the algorithm. 18

6.1 Preprocessing CT images, on the left the original image, on the right image after median filter. . . 20 6.2 Preprocessing CT images, on the left the thresholded image for the lung mask, on the

right image after closing for the inclusion of the vascular tissues and nodules, and the dilation for the inclusion of edges around the lung. . . 21 6.3 Candidate extraction using opening by reconstruction. On the left is the result after the

opening by reconstruction. On the right is the result after the regional maxima extraction. 22 6.4 Extraction of 2D images from volume planes, centred at the centroid of the candidate. . 22 6.5 Random examples of two dimensional nodule patches obtained from the annotated nodules. 22

7.1 Precision Recall curve obtained using the box constraint parameter 1×10−8 _{over the} validation set. . . 26 7.2 Precision Recall curve obtained using the box constraint parameter 1×10−8 _{over the}

test set. . . 26

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List of Tables

2.1 Summary for the methods, features, classifiers and database used in the algorithms in the reviewed literature. . . 5

4.1 Relevant parameters and degrees of satisfaction of each following the algorithms method-ology scheme . . . 14

5.1 Work plan for the dissertation, each activity is accompanied by the time it required for completion. . . 16

7.1 Evaluation of the recall varying the candidate extraction method performed over the first version of the database. TP = true positives, FP = false positives, FN = false negatives. . . 24

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Chapter 1

Introduction

1.1 Motivation and problem description

Lung cancer is the type of cancer with the third highest incidence and the highest mortality rate worldwide amongst men and women [2]. Statistically speaking, lung cancer has an incidence rate of 16.8% and a mortality rate of 23.6% for men, just below those corresponding to prostate cancer [2]. For women, the incidence rate for lung cancer is 8.8%, and the mortality rate is 13.8%, directly below those corresponding to breast cancer [2]. Its high mortality rate is due to the fact that most lung cancers are detected in late stages.

Early detection of these cancers is crucial in order to lower the mortality rate. Many of these cancers start off as nodules within the pulmonary parenchyma. Treating a lung cancer when it is a nodule provides the patient with a high probability of eradication of the cancer. Currently, radiologists use computed tomography (CT) scans in order to detect pulmonary nodules because this imaging modality allows the inspection of most of the morphological features the nodules present. Characteristics such as size, shape, localisation, density and borders are used by radiologists in order to determine if the pulmonary nodule could in fact be cancerous.

However, these nodules have a very small size, ranging from 3mm to 3 cm, which makes the task hard. Studies have reported that a trained radiologist will detect less than 60% of the pulmonary nodules in a CT scan [3]. It has also been reported that using a Computer Aided Diagnosis (CAD) software improves the accuracy of pulmonary nodule detection [4].

In this dissertation, computer vision techniques are used in order to localise potential pulmonary nod-ules from CT scans, represent their visual appearance, and classify them based on their predicted malignity. Experiments are conducted on the largest publicly available database for this task (LIDC-IDRI), consisting of 1009 lung CT scans, each of which is provided with the consolidated annotations of 4 radiologists. The difficulty of the task is highlighted by the fact that all specialists agree only on 39.27% of the total amount of annotated nodules. Such a low human consistency for this task stresses the need for automated and robust computational tools for the assistance in early diagnosis of lung cancer.

The algorithm’s main steps are presented in figure 1.1. First the algorithm preprocesses the images by applying a median filter. Afterwards, a mask for the lungs is extracted from each CT images. The candidates are extracted using template matching and opening by reconstruction. Subsequently, the features for each candidate are obtained using histograms of intensities, histograms of oriented gradi-ents, and pyramidal histograms of words. These features are classified using support vector machines, and finally they are evaluated using precision recall curves. The detailed description for each of these

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CHAPTER 1. INTRODUCTION 2

Figure 1.1: Algorithm overview figure.

stages will be presented in the following sections.

1.2 Scope and final products

This dissertation comprises the first stage of the development of the computer aided diagnosis software for the evaluation of lung nodules in computed tomography scans using computer vision and image processing techniques. The final products are algorithms written in Matlab for computed tomography analysis.

1.3 Objectives

1.3.1 General Objective

Create a computer aided diagnosis software for the detection and evaluation of pulmonary nodules from computed tomography scans.

1.3.2 Specific Objectives

Develop a computer vision system that localises pulmonary nodules in thorax computed tomography scans, analyses them according to their visual characteristics and predicts the malignancy of each of these.

Determine the agreement between the nodules detected by the algorithm, and the annotated nodules in each scan.

Determine the precision and recall of the computer aided diagnosis software for the detection of pul-monary nodules.

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Chapter 2

Literature Review

The nodule detection problem has been studied for over 15 years. Many investigators have published solutions to the problem implementing different image processing techniques. This section will describe the previous work in this field.

2.1 Algorithms for detection of pulmonary nodules

2.1.1 Lung nodule diagnosis using 3D template matching

In 2007, a group at University of Istanbul [5] developed an algorithm that uses interest regions of CT scans selected by radiologists to create a three dimensional image. Template matching is performed over the image in order to generate candidates. False positives were reduced using connected compo-nents and the sum of differences of densities in the surrounding pixels. Experiments were conducted on six images from the LIDC-IDRI database, where the method achieves a 100% sensibility and a 0.83 false positive rate per scan.

2.1.2 A Technique for Lung Nodule Candidate Detection in CT Using

Global Minimization Methods

In 2015 a technique for lung nodule detection in computed tomography scans using global minimisation methods was proposed [6]. This algorithm starts by extracting the chest wall, blood vessels, nodules and similar intensity objects using an active contour model with two regions. Afterwards, the nodules are separated from the surrounding tissue using mean curvature motion. In the next step, a rule based classifier is applied, which determines morphological features such as area, volume, circularity and elongation. They used 16 CT scans from the LIDC database as test, and obtained a 96% detection rate.

2.1.3 Fast lung nodule detection in chest CT images using cylindrical

nodule-enhancement filter

In 2013 a Japanese group [7] developed an algorithm for nodule detection. This algorithm segments lung regions from CT scans using thresholds. Afterwards, they use a cylindrical filter to highlight the nodules’ characteristics. Next they extract morphological features, and perform classification using a support vector machine. This group used 84 images from the LIDC-IDRI data base, achieving a recall

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CHAPTER 2. LITERATURE REVIEW 4

of 80% and a 4.2 false positive rate per image.

2.1.4 A new method for pulmonary nodule detection using decision trees

In 2013, another Japanese group [8] developed an algorithm that uses template matching for nodule detection, and decision trees as classifiers. Their database had 63 CT scans with a total of 95 nodules, and 75 non-nodules. These CT scans were taken at the Cerrahpasa Radiology Department, at the University of Istanbul. The candidates extracted by template matching are described using morpho-logical features such as area, perimeter, diameter, solidity, eccentricity, etc. Using their database, this algorithm achieves a 90.5% sensitivity and 87.6% specificity.

2.2 Algorithms for classification of pulmonary nodules

2.2.1 Intensity-based statistical features for classification of lungs CT scan

nodules using artificial intelligence techniques

Recently, in 2014, a group at the National Science and Technologies University at Pakistan [9] devel-oped an algorithm that uses a neuronal networks approach. They use 84 images from the LIDC-IDRI database. Their algorithm initially extracts the pulmonary volume, and using this pulmonary volume the candidates are extracted using multiple thresholds. The candidate’s morphological features are extracted and saved in feature vectors. These feature vectors are classified using a neuronal network. They determined that their algorithm reaches a sensibility of 96.95%, and precision of 96.68%.

2.2.2 Research on a Pulmonary Nodule Segmentation Method Combining

Fast Self-Adaptive FCM and Classification

Other methods have been implemented as well. In 2014 a fast and self adaptive Fuzzy C-Means pul-monary nodule segmentation method that combined clustering and classification learning was proposed [10]. This method segmented nodules that were adhered to blood vessels, pleura, and ground glass opacity nodules. This method was trained and tested over a total of 363 nodules obtained from the LIDC database. The results presented show that at least 70% of the detected nodules have a 50% error rate in the segmentation.

2.2.3 Automated system for lung nodules classification based on wavelet

feature descriptor and support vector machine

Orozco [11] developed an algorithm that uses a wavelet feature descriptor. This algorithm performs a supervised extraction of the region of interest using a Hough transform. After the preprocessing stage, the discrete wavelet transform is used to extract features, specifically the Daubechis wavelets transform. 19 features are computed from each wavelet sub-band, which are afterwards classified us-ing a support vector machine. The experiments were performed usus-ing 45 CT scans form the LIDC database, and 45 CT scans from the ELCAP database. The preciseness reported by the group is 82%, with 90.90% recall, and 73.91% specificity.

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CHAPTER 2. LITERATURE REVIEW 5

Table 2.1: Summary for the methods, features, classifiers and database used in the algorithms in the reviewed literature.

2.3 Literature Review Consideration

Although some of these algorithms show promising results, none of these were tested on the same database and the source code is not publicly available. This means that it is not possible to compare any one of the results to the other. Additionally, all of these methods are evaluated in small datasets, which may lead to overfitting. One of the main objectives of this dissertation is to standardise a database that allows the comparison of each method, and ensures that the amount of images evaluated is large enough to reduce the probabilities of overfitting the data. This is why it is important to use a standard database that is publicly available, it not only makes results comparable, but also ensures reproducibility of the method proposed.

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Chapter 3

Background Theory

3.1 Lung Cancer

As stated before, lung cancer is one of the most common malignancies. The lifetime risk for developing lung cancer is high for both men and women. 1 in 13 men and 1 in 23 women develop lung cancer in their lifetimes [2]. In the United States alone, 92,305 men developed lung cancer in 2005, approxi-mately 91,537 of these were expected to die because of this disease [12]. The risk for developing lung cancer augments with age; for every 100,000 men under 35 years, 7 develop this type of cancer, whilst for every 100,000 men aged over 75 years, 440 develop lung cancer. For women the rates are relatively lower, being 3 affected out of 100,000 for women aged below 35 years, and 72 out of 100,000 for women aged over 75 years [12]. This risk varies amongst the global population, and greatly depends on the smoking behaviour of each region [12].

3.1.1 Disease natural history

Cancer theories state that cancer grows from a single cell that has accumulated carcinogenic mutations. Following that theory, it would usually take 40 volume doublings (starting from a single cell) for a tumour to reach a 10 cm diameter, which corresponds to an average tumour size at death [12].Usually tumours are diagnosed at a size of 3 cm, when 33 volume doublings have already occurred. There are many histological types of lung cancer. One of these is small cell cancers. These are the most rapidly dividing cancer, which achieve volume doubling in as little as 29 days [12]. Generally, small cell tumours are detected 2 years after these were first formed [12].

Lung adenocarcinomas, another type of cancer, grow at a slower rate, achieving volume doubling every 161 days [12]. These types of cancer are detected when the volume is around 15.4 cm, which is a very large mass [12]. Death for this type of tumor occurs when the tumour reaches approximately 18 cm. Squamous cell carcinomas, one of the other types of lung cancer which shows very poor differentiation within the cells, doubles its volume every 88 days [12]. For this particular type of tumor, the diagnosis is usually made when the tumor has a 8 cm diameter. Patients that have this type of tumour usually die when the tumour reaches 9 cm in diameter [12].

This type of disease is usually diagnosed very late in its natural history. By the time of detection, these cancers would have probably invaded other tissues. Studies have shown that most tumours are capable of metastasising after 20 volume doublings have occurred. However, most of these malignancies are not detected until 30 volumen doublings have been reached [12]. Though lung cancers start off as single cells, detection can be achieved when these tumours are just pulmonary nodules, vastly reducing the

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CHAPTER 3. BACKGROUND THEORY 7

mortality rates by initiating early treatment plans.

3.1.2 Clinical features

The majority of patients who are diagnosed with lung cancer seek medical attention due to the symp-toms they are presenting. These sympsymp-toms include: chronic cough, occasional sputum production [12]. Some symptoms appear differently with each type of lung cancer. For example, excessive sputum and haemoptysis are presented with bronchoalveolar cell carcinoma. In some cases, chest pain appears, usually indicating invasion of the tumor to the surrounding tissues, such as the pleura. More acute pain may be felt when invasion of local structures, such as ribs, and vertebrae is present [12].

When the tumor has extended from the lung to other intra-thoracic regions, other symptoms may appear. When the pleura or the pericardium is involved, the patient may refer breathlessness and chest pain. The breathlessness may be explained by the pleural and pericardial effusions [12]. When tumours are located in the right upper lobe, their growth might compress the superior vena cava. These patients present a superior vena cava syndrome, which includes the following symptoms: headaches, facial fullness, plethora, oedema, congested veins in the neck and chest. Only 10% of the patients with small cell lung cancer present this particular syndrome [12]. Other apical tumours may involve the superior sympathetic chain, causing Horner’s syndrome. If the brachial plexus is compressed by the tumour, shoulder and neck pain may appear, as well as atrophy of the hand muscles [12]. Tumours that invade the mediastinum may cause dysphagia due to the compression of the oesophagus [12].

Metastasis to distant organs appears with a different set of symptoms. Patients usually present weight loss, which indicates a probable poor outcome. Usually, loosing more than 20% of the weight (compar-ing with a one month baseline) indicates a metastatic disease [12]. The most common metastasis site for lung cancer is the liver, which usually displays the associated weight loss. Bone metastasis may occur, usually in ribs, vertebrae, humerus or femur, causing localised pain [12]. Brain metastasis often results in symptoms such as confusion, personality changes, and seizures. Lung cancer can also spread to the adrenal glands, and the skin [12]. Para-neoplastic syndromes may also appear in patients with lung cancer. These are due to the production of ectopic hormones or peptides. This syndrome usually affects 10 to 20% of the patients with lung cancer [12].

3.1.3 Diagnosis

Although histopathological and cytological confirmation of the diagnosis is required in order to begin management with lung cancer patients, the first step towards diagnosing a patient involves medical imaging [12]. The initial diagnostic test performed should be a computed tomography scan of the thorax and abdomen, as well as a chest radiography. The CT study will allow to locate the primary lesion and determine the adjacent structures that may be affected [12]. For patients that may have a suspected lung cancer, a fibreoptic bronchoscopy may be preformed in order to visually examine the mayor airways and sample abnormal tissue for cytological examination[12].

3.2 Computed Tomography

Computed tomography was the first method that acquired non-invasive, non-superimposed images of the inside of the human body [13]. Conventional radiographs portray three-dimensional objects as a two-dimensional image, superimposing tissues. Computed tomography achieves non-superimposed images by scanning thin sections of the body with a narrow x-ray beam that rotates around the body, producing a cross-sectional image [1].

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Figure 3.1: Data CT image viewed as voxels, or as pixels when analysing a CT slice [1].

The first scanners had limitations in the way the slices could be performed, displaying only axial cuts. However, newer scanners allow representation of more planes other than the transverse plane [1]. Most scanners use a continuous acquisition method for scanning calledspiral,helical, orisotropic. Each CT image is evaluated using three specific criteria. These are: spatial resolution, low contrast resolution and temporal resolution [1].

Spatial resolution describes the ability a scanner may have to define small objects in a distinct man-ner. On the other hand, low-contrast resolution refers to the ability a scanner may have to distinguish objects with similar densities on the same image. Temporal resolution refers to the speed in which the data can be acquired. This speed closely relates to the reduction of artifacts that result from object motion, like breathing or the heat beating [1].

Computed tomography uses a computer in order to process the signals obtained from the passage of the x-ray beam through the body. The computer processes the data, and generates an image that represents the cross-section. Each slice shows only the parts of the anatomy at that level of the body, representing a specific plane in the body of the patient. The thickness of the plane is calledZ axis, which defines the thickness of each slice. This thickness will limit the x-ray beam so that it only passes through this volume, reducing superimposition of structures and radiation scattering [1].

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All the data that forms a CT image may be sectioned into elements in other axis. TheX axis repre-sents the width, while theY axis represents the height. This means that a CT image is volumetric, the unitary volume element that conforms the image is calledvoxel. Usually, the matrix grid that forms each CT slice has 512 rows and 512 columns, which would account for a total of 262,144 pixels per slice. When evaluating slices the units are two-dimensional, therefor called pixels, as shown in figure 3.1 [1].

3.2.1 Beam attenuation

CT images show varying shades of grey depending on the structure represented. The different intensi-ties are generated based on radiation principles. The x-ray beam that is directed toward the body may pass through, may be scattered, or may be absorbed. Due to the fact that the beam is composed by photons, each structure may absorb different amounts of these. As a result, the beam is attenuated. The beam passes through the body, and detectors on the other side record the amount of photons that made it through [1].

When photons pass from the emitter to the detector unimpeded, the image for that section results in a black area, or an area with low attenuation. On the other hand, when an x-ray beam cannot be sensed in the detector, the area in the image results white, representing a high attenuation of the beam [1]. The amount of photons that interact with a structure directly depend on the thickness, atomic number, and density of this object. Density physically corresponds to the amount of mass in a given volume. Dense elements that have a high atomic number usually posses many electrons and a heavy nucleus, which facilitate the interaction with protons [1].

3.2.2 Hounsfield units

In a CT scan, multiple degrees of attenuation are available. In order to compare the densities be-tween the tissues, these attenuations may be quantified using Hounsfield units. Hounsfield units are used to quantify the degree in which a given structure attenuates an x-ray beam. They are named after Godfrey Hounsfield, one of the pioneers in the development of the CT scanner. He arbitrarily assigned values to the attenuations of different substances and structures. For example, 0 represents the attenuation of distilled water, 1000 represents the attenuation of dense bone, and -1000 represents the attenuation of air. For all anatomic structures, Hounsfield units fall into the -1000 to 1000 range [1].

Hounsfield units (HU) vary directly with the linear attenuation coefficient. 1 HU is equivalent to 0.1% difference between the linear attenuation coefficient of the tissue compared to that of water. Using the HU it is possible to estimate the composition of the structures that appear on a CT image [1].

3.2.3 Volume averaging

When a CT scan is ordered for a patient, a series of slices are performed over the area of interest. Depending on the structure of interest, certain parameter may be adjusted when taking the CT slices. For example, when a smaller object is being scanned, a thinner CT slice is configured so that the probability of missing the structure is reduced [1].

Although modern CT scanners allow taking slices very quickly, and these can be as thin as 1 mm, the radiation dose when taking these slices is augmented. This is why generally when the structures of interest are small, the region scanned is not extensive. Conversely, when the region of interest is very large, the slices are taken up to 7 mm apart from each other, allowing the complete analysis of the

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region while reducing the radiation dose [1].

3.2.4 Computed tomography system operation

The CT scanner market is very wide nowadays. Each manufacturer designs their scanner in a different way, which essentially involves changes the mechanical makeup and configuration of the scanner per se. However, the basic mechanism behind the function of all scanners is practically the same.

CT scanners use x-ray beams in order to produce their final images. For these beams to be produced, x-ray photons need to be created. These photons are generated when fast-moving electrons collide against a metal target, allowing their kinetic energy to be transformed into electromagnetic energy. In a CT scanner, the x-ray tube contains filaments that provide the electrons necessary for the creation of x-ray photons. The filament is heated until electrons gain enough kinetic energy to hover around the filament in the space cloud. At this point, the generator produces high voltage and transmits it to the x-ray tube. This high voltage will propel the electrons towards the anode[1].

The anode is the focal spot, where the electrons collide and produce the x-ray beam. The amount of electrons that strike is controlled by the current, measured in milliamperes. X-ray photons pass through the patient and arrive at the detector, where the energy of the photons detected is converted to light. A photodiode in the detector receives the light produced by the photons, and transforms it into an electric current. Each detector cell is sampled and an analog-to-digital conversion occurs. The conversion is made by the data acquisition system, or DAS. The signal from the DAS is transmitted to the central processing unit (CPU) which performs the image reconstruction [1].

The processor uses individual views in order to reconstruct the densities from each slice. This informa-tion is translated to a matrix which facilitates the image formainforma-tion process. Each posiinforma-tion in a matrix represents a pixel in the picture, each one is associated to a HU. This digitalised data is sent to the display processor which converts Hounsfield units into shades of grey [1].

3.3 Computer Vision Techniques

3.3.1 Morphological Reconstruction

Morphological reconstruction is a transformation that uses two images and a structuring element. The two images are a marker, which sets the starting point for the reconstruction, and the mask, which sets the boundaries for the transformation. If the marker is F, and the mask is G, the reconstruction of G from F,RG(F) , follows through the iterative steps:

1. The marker image F corresponds to a subset of G.

2. The structuring element is defined.

3. h1is set to be F

4. The following step is repeated

hk+1= (hk⊕b)∩G

untilhk=hk+1 5. Finally,RG(F) =hk+1

In the opening by reconstruction method, the marker image F corresponds to an erosion of G. The equation can then be written as:

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RG(G B)

which can be implemented in Matlab using:

>> F = imerode(G, B)

>> Rg = imreconstruct (F, G)

where G and B would be previously defined [14].

3.3.2 Template Matching

Template matching is used in order to establish a correspondence between two images using normalised cross correlation. This technique uses a template image,I0, and slides it across the imageI1establishing a correlation between the template image and the patch ofI1that is evaluated. The purpose of doing so is to find the patch ofI1 that containsI0 . The normalised cross correlation can be implemented using the following equation:

EN CC(u) =

P

i[I0(xi)−I0¯][I1(x1+u)−I1¯]

q P

i[I0(xi)−I0¯]2

q P

i[I1(xi+u)−I1¯]2

Where the mean images of the patches are represented by:

¯

I0= _N1 P_iI0(xi)

¯

I1= _N1 P_iI1(xi+u)

in these equations N represents the number of pixels in the patch. These correlation scores are always between the range [-1, 1], which facilitates handling the scores. A score of 1 would represent total match of theI1 patch withI0[15].

3.3.3 Pyramidal Histograms of Words

Histograms of words, also known as Bag of words, is an algorithm used for category recognition. This algorithm computes the distribution for a set of visual words in an image. Afterwards, the algorithm compares the distribution obtained to the distribution observed in the training images. In order to do so, the algorithm first extracts the SIFT descriptors from the patches of interest in the training images. Then, it applies k-means clustering to construct a visual vocabulary. When a query image is evaluated, the visual vocabulary is used in order to make a histogram. Finally a support vector machine is used for classification of the query image. This particular support vector machine uses a kernel appropriate for determining distances between histograms, like a chi square distance [16].

3.3.4 Histograms of Oriented Gradients

The Histogram of Oriented Gradients (HOG) is an algorithm that is used for category recognition. This algorithm divides the image window in cells. In each of these cells it accumulates de magnitude-weighted votes for gradients in the leading orientation. The gradients are obtained after convolving the cell image with the following filters:

The final gradient for a pixel corresponds to the color channel with the greatest magnitude. For each cell a histogram is built, using the magnitudes obtained for each gradient orientation. These histograms are then normalised, and concatenated into a single feature vector. After these descriptors have been computed, a support vector machine is trained using the descriptor vectors.[17]

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3.3.5 Support Vector Machines

Support vector machines are supervised classification models that analyse a set of data with its asso-ciated labels into two separate classes. The classification is achieved when the support vector machine finds the best hyperplane that separates all data point belonging to one class from the other class. Said hyperplane is the one that has the largest margin between the two classes that are evaluated. The margin refers to the maximum width of the block that is parallel to the hyperplane, and that contains no data points. The data points that are the closest to the hyperplane, in the margin of the block, are called the support vectors [18].

Support vector machines classify binary problems, defined by

D={(xi, yi)|xiRp, yi{−1,1}}n_i₌₁

where x corresponds to the training data vectors, y corresponds to their categories, these being 1 or -1. In order to find the hyperplane, the following equation is used:

hw, xi+b= 0

hw, xicorresponds to the inner dot product ofw and x. b is a real number. In order to find the best hyperplane, the support vector machine must find thew andb that will minimizekwk such that for all data points

yi(hw, xii+b)≥1

where the support vectors correspond to thexi that are on the boundary, in other words, where

yi(hw, xii+b) = 1

3.3.6 Evaluation

Due to the fact that the problem at hand is an unbalanced problem between classes, the evaluation of the performance of the algorithm uses precision-recall curves instead of ROC curves. Precision stands for the positive predictive value, while recall corresponds to the amount of positive candidates that are extracted. The equations to describe them are as follows:

P recision= T rueP ositives T rueP ositives+F alseP ositives

Recall= T rueP ositives Groundtruth =

T rueP ositives T rueP ositives+F alseN egatives

The plots evaluated will have Recall in the horizontal axis, and precision in the vertical axis. For any problem, the objective is to reach the (1,1) region of the plot, which accounts for 100% precision and recall. This would imply that all the nodules are being detected (recall), and that all of them are recognised as such (precision).

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Chapter 4

Work Definition and Specifications

4.1 Definition

This work intends to approach the problem of pulmonary nodule detection from CT scans in order to improve overall performance depicted by the radiologists. This work aims to develop a computer aided diagnosis software that may be used in the clinical setting by the radiologists in order to detect the greatest amount possible of pulmonary nodules in a CT scan. The undergraduate dissertation is directed towards the generation of the algorithms for the first version of the software using computer vision techniques.

4.2 Specifications

In this dissertation the algorithm will be produced, however the clinical implementation of the soft-ware will not, due to the fact that a clinical application will require testing that exceeds the semester destined for the development of the undergraduate thesis. The following table includes the relevant parameters and the different satisfaction levels in each of them for the evaluation of the algorithm developed.

These parameters are established following the methodology for the algorithm development. If each of the parameters set on Table 4.1 is accomplished, the degree of satisfaction will serve as a measurement of the overall performance of each step. Additionally, all of the parameters are within the boundaries placed for studying the problem.

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CHAPTER 4. WORK DEFINITION AND SPECIFICATIONS 14

Table 4.1: Relevant parameters and degrees of satisfaction of each following the algorithms methodol-ogy scheme

Parameter Ideal Acceptable Minimum required

The algorithm performs an adequate candidate extract-ion throughout the database that includes the nodules an-notated by the radiologists

Within the candidate extrac-tion, 90% to 100% of the an-notated nodules are included

Within the candidate extrac-tion, 90% to 70% of the anno-tated nodules are included

Within the candidate extrac-tion, 70% to 60% of the an-notated nodules are included

The algorithm can represent each candidate with charac-teristic features

3D candidates are represen-ted using 3D feature descri-ptors.

3D candidates are represen-ted by three planes centred in the nodule’s centroid, and 2D feature descriptors are used

3D candidates are represen-ted by one image plane, and 2D feature descriptors are used

The algorithm classifies the candidates according to their features

A numerical value indicating the malignancy of each candi-date is calculated

A binary classifier is trai-ned in order to classify ca-ndidates into nodules or non-nodules

Separate classifiers are used for each feature in order to classify candidates into no-dules or non-nono-dules. A gro-uping classifier is used in order to determine the weigh-ts of each classifier

The performance of the al-gorithm is evaluated using precision and recall terms

The precision and recall achieved by the algorithm su-rpasses those of the medical physician

The precision and recall achieved by the algorithm are below the medical physi-cian’s, and above those achi-eved by chance.

The precision and recall a-chieved by the algorithm are below those achieved by the medical physician, and below or equal to those achieved by chance.

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Chapter 5

Methodology

The methodology followed for developing the algorithm is presented in Figure 5.1. The initial steps required gathering background information in order to determine the achievements in this field, and current status of the problem. This step also served to establish the most commonly used databases for the development of the algorithms, and their respective sizes. Many databases were investigated, in-cluding the NELSON study, LUNGx, LIDC, ELCAP, PHANTOM, QIN. For each of these databases, images were analysed based on their resolution, expedition date, annotation type, and size of the complete database. The Lung Image Database Consortium (LIDC) database was chosen, it cointains 1006 lung CT images with pulmonary nodule annotations. These annotations are performed by four different radiologists.

The following step was destined only for the selection of the database, and database standardisation. This standardisation allows the evaluation of any CT image, with no limitations regarding individual machine setup. The database was divided as follows: 50% for the test set, 25% for the training set, and 25% for the validation set. The next step is the preprocessing step. In this step a 3D median filter is applied to the volumes in order to reduce noise and false positives. Additionally, a lung mask extraction is performed. This was done in order to reduce false positives, and to concentrate all efforts of nodule detection specifically in the pulmonary parenchyma.

After the preprocessing stage, nodule candidates are extracted. Two different methods were evaluated: opening by reconstruction and template matching. These two methods were compared using three pa-rameters: time elapsed in candidate extraction per image, recall over the complete training set and the total false positives over the training set. Using the extracted candidates features are extracted. Two methodologies were applied, histograms of oriented gradients and pyramidal histograms of words. Both descriptor vectors were concatenated in order to use one feature vector.

For classification, a support vector machine is used. This support vector machine classifies nodules vs non-nodules. The validation set was used in this stage in order to establish the best box constraint parameter. Finally, an evaluation was performed over the test set, and precision recall curves were calculated over the test set and the validation set.

5.1 Work Plan

The work plan followed during the development of this dissertation is detailed in Table 5.1. In the table, each activity is accompanied by the total amount of time taken up for the development of the base algorithm. Most of the developed algorithms have required small changes overtime. Additionally, many of these were developed simultaneously.

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CHAPTER 5. METHODOLOGY 16

Table 5.1: Work plan for the dissertation, each activity is accompanied by the time it required for completion.

Activity Time Elapsed

Background information research 20 days

Database Selection and Download 10 days

Database Standardisation 6 days

Algorithm for candidate extraction 60 days Algorithm for evaluation of candidates 3 days Algorithm for HOG feature extraction 5 days Algorithm for PHOW feature extraction 5 days Algorithm for classification of candidates 6 days

Algorithm for validation 6 days

Bootstrapping algorithm with subset of false positives 10 days Algorithm for precision recall evaluation 3 days

Additional adjustments 15 days

Document and presentation 15 days

Every monday meetings took place in order to evaluate the progress and establish further advance-ments. A progress report and a presentation were evaluated on March fifth of the present year. The final presentation was evaluated on May twentieth of the present year.

5.2 Information Sources

Various toolboxes were used in order to develop the algorithm. Particularly, computer vision toolboxes such as VL FEAT [19]. This toolbox was used for the implementation of the feature extraction using HOG and PHOW. It was also used for its SVM, and for the calculation of chi squared distances. Piotr Dollar’s image processing toolbox [20] was used for the three dimensional template matching function. A three dimensional median filter from an external source was also used [21].

The academic preparation received in the past year through the image processing and computer vision courses was essential for the development of the algorithm. This preparation provided the guidelines to understand the methods implemented in this dissertation. The dissertation Advisor’s contributions towards the development of the algorithm are invaluable. His advice, availability, teaching disposition, and great knowledge made this development possible.

5.3 Considered Alternatives

Within the algorithm several alternatives were considered. Firstly, for candidate extraction the meth-ods opening by reconstruction and template matching were evaluated. The result over the recall favoured template matching. However, this method showed six times more false positives than the opening by reconstruction. Each image analysis takes around three minutes to complete during the template matching method, and less than a minute for the opening by reconstruction. And, because the advantage over the recall was 2%, it was considered to be more beneficial to consume less time and posses less false positives within the extracted candidates.

For feature extraction, histograms of intensities were evaluated at first. However, due to the great variability within the database the intensity analysis did not show promising data. In exchange, HOG

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and PHOW features were used. Using the last two features showed a significant reduction in the total amount of false positives within the candidates that were extracted.

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Chapter 6

Proposed Method

The algorithm developed is divided into the following steps: preprocessing, candidate extraction, feature extraction, classification, validation and evaluation. This section will explain the preprocessing algorithm, the candidate extraction algorithm, the feature extraction algorithm, the classification algorithm, and the evaluation algorithm.

6.1 Preprocessing

Before candidates extraction, the volumes are filtered using a 3 by 3 by 3 median filter using the functionmedfilt3.mdeveloped by Damien Garcia [21]. the purpose of implementing the median filter is to reduce noise that may be present in the CT scans, while preserving edges (Figure 6.1). After applying the median filter, the lungs are extracted from the volume. This is achieved by thresholding the lung volume using the Hounsfield Scale. However, due to the fact that not all CT scans come from the same machine, the Hounsfield Scale varies from one image to another. An example of the lung mask is displayed in Figure 6.2.

6.2 Candidate Extraction

Two methods were used for candidate extraction, these were opening by reconstruction and template matching. The algorithms implemented in each of these are explained below.

Opening by Reconstruction

The algorithm for candidate extraction using opening by reconstruction starts by creating the marker volume, by eroding the lung mask with a ellipsoid of radius 1 pixel and height z pixels, where z is the separation between each slice cut. The Matlab function used isimerode. The volume is reconstructed using the matlab functionimreconstruct. This function receives the marker volume, the volume, and the connectivity as inputs. Then, the regional maxima are calculated for the output, in order to find the highest response regions in the volume.

Afterwards,bwconncompis used to extract the connected components, and withregionpropsthe cen-troid of each connected component is calculated. These connected components and their corresponding centroids are the candidates.Figure 6.3 illustrates an example of opening by reconstruction and the regional maxima extraction.

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CHAPTER 6. PROPOSED METHOD 20

Figure 6.1: Preprocessing CT images, on the left the original image, on the right image after median filter.

Template Matching

In order to implement template matching in a volume, Piotr Dollar’snormxcorrn.m from his Image Toolbox was used [22]. The template used for the correlation corresponds to the same ellipsoid used in the aforementioned algorithm, with radius 1 pixel and height z pixels. After the correlation is made, the regional maxima are extracted. Subsequently, the connected components are extracted, and the centroids for each of these are calculated. The connected components and the centroids are the candidates.

6.3 Representation

In order to represent the candidates, and facilitate the processing, the candidates were evaluated by the algorithms using two dimensions rather than three. Three 2D images represent each candidate, one for each plane (x,y,z), as shown in Figure 6.4. An example of images taken from the radiologist’s annotations can be seen in Figure 6.5.

For the histograms of oriented gradients part of the algorithm, the VLFeat library is used [19]. Similar to the histograms of intensities, the first part of the algorithm generates a standard HOG feature histogram from the annotated nodule images. In order to do so, thevl_hogfunction is used to extract the features from all of the candidates. After extracting the HOG features, the histograms for the same nodule will be concatenated. For the second part of the HOG algorithm, the HOG features are extracted for all the candidates. Using these histograms and their labels, a Support Vector Machine is trained over the Train subset. The adjustments to the regularization parameter are made by evaluating in the validation subset.

Finally, for the pyramid histograms of words, also uses the VLFeat library [19], for the vl_phow

function. The first step in this algorithm is the creation of the visual vocabulary. The visual vocabulary is created form the images of the annotated nodules, using thevl_phowfunction. All the descriptors from the same nodule are concatenated. Afterwards, k-means clustering is applied in order to obtain 600 words. The second part of the algorithm calculates the histograms of words on the train subset using Andrea Vedaldi’sgetImageDescriptorfunction. The final part of the algorithm implements a Support Vector Machine in order to classify the data. The Support Vector Machine uses the histograms

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Figure 6.2: Preprocessing CT images, on the left the thresholded image for the lung mask, on the right image after closing for the inclusion of the vascular tissues and nodules, and the dilation for the inclusion of edges around the lung.

and the labels for all the train data, and adjustments to the regularization parameter are made upon the validation subset.

Hard negative mining is used in order to achieve the best hyperplane that separates nodule data from non nodule data.

6.4 Classification

A unique support vector machine is trained using the concatenation of all the feature histograms. Using all the features, the support vector machine determines the appropriate weight for each feature descriptor. The output of the trained Support Vector Machine is a confidence measure on wether or not a candidate is a nodule.

6.5 Evaluation

The evaluation of the performance of the algorithm uses precision-recall curves. Precision stands for the positive predictive value, while recall corresponds to the amount of positive candidates that are extracted. The equations to describe them are as follows:

P recision= _{T rueP ositives}T rueP ositives₊_{F alseP ositives}

Recall= T rueP ositives_Groundtruth =_{T rueP ositives}T rueP ositives₊_{F alseN egatives}

The plots evaluated will have recall in the horizontal axis, and precision in the vertical axis. For any problem, the objective is to reach the (1,1) region of the plot, which accounts for 100% precision and recall. This would imply that all the nodules are being detected (recall), and that all of them are recognised as such (precision). In the real world, the measure taken would be the AP or the F score, which would correspond to the point in the curve closest to the (1,1) mark.

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Figure 6.3: Candidate extraction using opening by reconstruction. On the left is the result after the opening by reconstruction. On the right is the result after the regional maxima extraction.

Figure 6.4: Extraction of 2D images from volume planes, centred at the centroid of the candidate.

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Chapter 7

Experiments

7.1 Evaluation Methodology

7.1.1 Database

As stated before, the database selected for developing and evaluating the algorithm is LIDC-IDRI. This database was put together with the purpose of serving as a referential database for the develop-ment of computer aided diagnostic methods for lung nodule detection, classification, and quantitative assessment [23]. This database was initiated by the National Cancer Institute of the United States of America, and was given completion through the Foundation for the National Institutes of Health, and the Food and Drug Administration[23]. This database includes 1006 CT scans, each of these annotated by four experimented thoracic radiologists [23].

In the initial phase, a blinded-read was performed by the radiologists, these annotated each nodule as nodules smaller than 3 mm, nodules larger than 3 mm, and non-nodule larger than 3 mm [23]. In the second phase, each radiologist evaluated their own marks, as well as the marks of other radiologists for the same image and rendered a final opinion [23]. The database contains a total of 7371 lesions marked as nodule by at least one radiologist, 2669 lesions marked as nodule larger than 3 mm by at least one radiologist, with 928 of these marked by all radiologists. The rate of agreement between the experienced radiologists is only 34.7%, which reinforces the clear difficulty of the task. This is a publicly available database which serves the purpose of guaranteeing the repeatability of the results.

7.1.2 Methodology

For this algorithm the database was divided as follows: 50% for test, 25% for train, 25% for validation. The initial division was done as follows, for all images ordered by name in ascending order, one image would be assigned to test and the other to train. Afterwards, the training dataset was divided into train and validation in the same manner, assuring that all the images would be uniformly distributed amongst all sets. A first evaluation will be made from the candidates extraction, to obtain the initial recall of the algorithm. Afterwards, another precision-recall evaluation is performed after the classifi-cation by the Support Vector Machine.

7.2 Validation Results

The first step performed was the evaluation of the total recall performed over the extracted candi-dates. Eight variations of the algorithm were evaluated on a subset of the train set. These variations

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CHAPTER 7. EXPERIMENTS 24

Table 7.1: Evaluation of the recall varying the candidate extraction method performed over the first version of the database. TP = true positives, FP = false positives, FN = false negatives.

Method Structuring element Recall T P F P F N

Opening by reconstruction

Median Filter Ellipsoid, (1, 1, 1) 86.55% 2130 10619525 331 Ellipsoid, (1, 1, z) 88.66% 2182 58581375 279

No Filter Ellipsoid, (1, 1, 1) 73.10% 1799 3852602 662 Ellipsoid, (1, 1, z) 85.29% 2100 136218956 361

Template Matching

Median Filter Ellipsoid, (1, 1, 1) 90.13% 2218 501121248 243 Ellipsoid, (1, 1, z) 80.13% 2218 964357534 243

No Filter Ellipsoid, (1, 1, 1) 90.13% 2218 1421185243 243 Ellipsoid, (1, 1, z) 90.13% 2218 592341614 243

included using opening by reconstruction or template matching, applying a median filter or not, and variations in the structuring element. The results are shown in Table 7.1. The best results are obtained when using opening by reconstruction with median filter preprocessing, and an ellipsoidal structuring element. This method was then evaluated using the full train set, the total recall obtained was 96.75%.

The validation was performed using the support vector machine trained with the training set using different regularisation parameters. The regularisation parameter chosen was 1×10−8 _{because it} re-sulted with the least amount of error. Each of these support vector machine was trained using all the train positives and 30000 random false positive nodules, and validated over all the validation set positives and 30000 random false positive nodules. This was performed because of memory limitations.

Figure 7.1 portrays the precision recall curve obtained using the selected box constraint parameter for the support vector machine. As may be observed, for the maximum recall, 96.75%, the precision obtained over the validation dataset is 5.556%. This precision represents a total reduction of the false positives by a factor of 3270, as shown in equation 7.1.

0.0556

1.7×10−5 = 3270.588 (7.1)

7.3 Test Results

Using the trained support vector machine with the box constraint parameter at 1×10−8_{, the test} set was evaluated. For the test set, each CT scan was evaluated independently, which allowed for the complete inclusion of the false positive detections. The precision recall curve obtained is shown in Figure 7.2.

As can be seen, the total precision for the test set is 0.21×10−3, which is lower than that obtained using the dataset. However, for the test set all of the false positives were evaluated, this could account for the reduction observed between the validation set and the test set. The reduction of false positives over the test set is equivalent to a factor of 12 (equation 7.2). It is not as large as the reduction observed over the validation set, however the test set is containing all the false positives.

0.00021

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7.4 Evaluation of the Work Plan

The work plan was fully completed, all the activities were performed, and the respective tests were evaluated. The work plan served as a starting point and as a measurement of progress during the development of the project. Although in many occasions previous algorithms had to be modified in order to adjust to newer versions of the standardised database, these modifications were minimal, and did not reflect setbacks in the schedule. Most of the work plan was successfully completed due to the virtual machine assigned from the University Cluster, the processing capabilities and the memory available for these eased the processing time and the amount of simultaneous codes that could be tested.

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Figure 7.1: Precision Recall curve obtained using the box constraint parameter 1×10−8 _{over the} validation set.

Figure 7.2: Precision Recall curve obtained using the box constraint parameter 1×10−8 over the test set.

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Chapter 8

Discussion and Conclusions

The proposed algorithm was developed, trained, validated and tested using the LIDC-IDRI database. As such, a 96.75% recall was achieved, which means that out of all the nodules in the database, the algorithm is detecting 96.75% of these, along with false positives. The remaining issue is reducing the amount of false positives within the candidates. With the HOG and PHOW features a reduction was achieved. The validation set showed a total reduction by a factor of 3270. This number could be correlated to the amount of false positives given a true positive. With the candidate extraction algo-rithm, for every true positive there would be 60000 false positives; after applying the HOG and PHOW features to the images of the validation set, for every true positive, there would be approximately 19 false positives.

It is important to highlight the fact that during the validation, only a subset of the false positives were used. For the test set, all the false positives were passed through the support vector machine. The results, though lower than those obtained from the validation set, were still positive. In the test set, a reduction by a factor of nearly 12 was portrayed. The results from the test and the validation set are indicating that HOG and PHOW features are reducing the amount of false positives in the image, though not in the rate expected in order to use this software in the clinical setting. The vast amount of false positives are limiting the immediate clinical application of this software.

However, some changes are currently being performed in order to augment the factor by which the false positives are reduced. Bootstrapping is being implemented in order to retrain the support vec-tor machine using the negatives that confuse machine the most. In this way, a new hyperplane will be constructed, hopefully a more adequate one for classifying nodules. Additionally, more complex descriptors and classifiers, such as convolutional neural networks (CNN) may be trained in order to evaluate if the problem presents a more viable solution through that perspective.

Although the software developed does not show the positive outcomes that the ones described in the literature refer, it is important to recognise that this algorithm has been developed using a total of 1006 CT scans. Training and testing in this database would more likely guarantee that a software would perform similarly in a real setting as it does in the test subset. Likewise, it is possible that the other softwares, when tested in this database, would perform differently. In order to compare results the algo-rithms for the softwares described in the papers would have to become available. There is a possibility that using only 6 or 84 images could result in data overfitting. This would generate positive results in the small database they used for testing and training, and display drastic differences in external images.

Comparing the results obtained with the initial proposed objectives, a computer aided software for the detection of pulmonary nodules from computer tomography scans was developed. The evaluation per se has not yet been implemented due to the fact that further elimination of false positives has to be

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CHAPTER 8. DISCUSSION AND CONCLUSIONS 28

performed before evaluating the malignancy of a pulmonary lesion. The computer vision system that localises pulmonary nodules was developed, however the malignancy prediction, as stated before, has not yet been implemented. The evaluation of the software was performed in every step. This would fulfil the determination of the agreement between candidates and annotations objective, as well as the analysis for precision and recall. The algorithm for displaying the pulmonary nodules to the specialist was developed, further work on the algorithm and user interface has to be done. This algorithm will be available once the reduction of false positives has been obtained.

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Chapter 9

Future Work

For further work, the bootstrapping algorithm will be finished and tested in order to compare the overall improvement in the classifier. Additionally, a more complex descriptor and classifier will be implemented in order to evaluate if the problem would benefit more from these tools. On the other hand, the investigation groups that have developed software in this particular area are being contacted in order to compare results using their same database, or use their code on this database.

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Development of a computer aided diagnosis software for the evaluation of lung nodules in computed tomography scans