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This section presents the most relevant key work regarding lesion classification based on ROI selection, according to the categorisation shown in Figure3.1. Hence, Bag-of- Visual-Features will be introduced in this section.

The Bag-of-Visual-Words (BoVW) can be used for image classification, through treating image features as words. The ROI is abstracted by several local patches to generate a numerical vector called feature descriptors (Winn et al.,2005). The feature descriptors-represented patches are converted to codebook (Van Gemert et al., 2010) which is considered as a representative of several similar patches in the feature space.

Image patch representation and bag-of-visual-words (BoVW) were used in lesion classification by analysing multiple region of interest, (Safdari et al., 2013) proposed classification and lesion detection system, where a visual word histogram was used to build a dictionary through using local descriptors and representing a region in the image. The accuracy result reported is more than 95% on dataset size of 73 CT images. (Wang et al., 2015b) proposed CAD system to classify normal livers and livers with lesions. The dataset comprised 151 cases (76 normal and 75 abnormal). The patches are extracted densely from a given ROIs with 400 to 500 patches. The histogram of oriented gradients (HOG) and intensity are extracted as the features of patches inside ROI. The coding dictionary is created from feature clustering in the training set. Each patch feature is coded with sparse constraint to generate a coding scheme from both training and testing images. Bag of visual features (BOF) is used to represent the ROI. The SVM is adopted for classification with accuracy of 96.15%.

Regarding to recent developed BoVW, (Diamant et al., 2016) proposed single dic- tionary BoVW for the automatic lesion classification. Two datasets were used to eval- uate the system performance with total number of 194 CT images. The visual word histograms are generated based on creating two separate dictionaries from two ROIs for interior and lesion margin region where all the patches inside ROIs are clustered by using k-means algorithm. The two histograms from interior and margin lesion are concatenated to build a new feature vector that represents the given lesion. The proposed method shows result with 93% of accuracy by using nonlinear SVM with histogram intersection kernel. The best accuracy was obtained using the combination of two datasets parameter: (1) patch size 7x7 pixels, dictionary size of 160 words and word size of 14 for the first dataset and (2) patch size 9x9 pixels, dictionary size of 200 words and word size of 12 for the second dataset.

An approach similar to the preceding ones was used by (Diamant et al.,2015). In their work, the analysis of BoVW was performed on single (portal phase) and multi- phase CT scans. The given ROI is divided into uniform patches (patch size between 5 and 13 pixels), from which visual words are computed. The histogram of visual word is utilised to generate feature vectors for each image. A mutual information (MI)

criterion was used to improve BoVW model by selecting the most relevant words from a generated dictionary. For classification task, the SVM with histogram intersection kernel was used. The experiments on 85 CT images, used optimal parameter of BoVW- MI (patch size of 11, visual of word size of 10 principal component analysis (PCA) coefficient and dictionary size 260 words), gave the weighted average classification sensitivity and specificity of about 82.4% and 92.7% for multi-phase CT scan and 70.6% and 86.9% based on single phase. This work was later tested by (Diamant et al.,2017) on three different tasks: chest x-ray pathology identification, liver lesion classification with dataset size 118 portal phase CT images and lesion classification in breast mammograms, by considering different parameter of BoVW for each task. Table3.1represents a different classification system based on bag-of-visual features.

In summary: BoVW and image patches are a crucial approach to any classifi- cation system. The BoVW were originally proposed for text document analysis, and it was further adapted for image analysis (Bosch et al., 2007). The existing methods tried to capture all the characteristics of lesion through dividing the ROI into patches (Diamant et al.,2016). However, the widely used BoVW approach with image patches for lesion classification basically (1) uses k-means for coded vector calculation to gen- erate sparse dictionary learning (codebook learning) (Jurie and Triggs, 2005), which approximates any local descriptor using one learned visual word only and leads to large reconstruction error of local descriptors (Wang et al., 2010,2017); (2) Through literature, there exists a limited work based on extracted patches that the accuracy of the existing methods mainly depends on the number and size of the patches in addition to the dictionary size , which may work only with specific conditions such as specific dataset and specific machine settings. As a consequence, the performance is varied significantly under ROI selection (Singh et al.,2014) and different acquisition condi- tions (Bharti et al., 2017) such as different operators and settings. In proposed work, the multiple ROIs (internal, border, and surrounding area) fused with the difference-of- features between the internal lesion and surrounding area employed as a new feature vector to well-represent the lesion characteristic, and the various behaviours between benign and malignant.

Chapter 3. Literature Re vie w

Author Year Dataset Patch

size

Number of dic- tionary word

Features Accuracy

(Safdari et al.,2013) 2013 Cyst (25); Metastases (24); Haemangiomas (24)

9x9 250 Visual word his-

togram

95.89%

(Wang et al.,2015b) 2015 Normal (76); Abnormal (75) 16x16; 24x24; 32x32 500 HOG; Intensity 96.15% (Diamant et al., 2015) 2015 Haemangiomas (27); Focal Nodular Hyperplasia (16); HCC (29); Cholangiocarci- noma (13)

11x11 260 Visual word his- togram

Portal phase (Sensitiv- ity 70.6%; Specificity 86.9%); Multi-phase (Sensitivity 82.4%; Specificity 92.7%) (Diamant et al., 2016) 2016 Cyst (61); Haemangiomas (53); Metastases (80)

7x7 160 Visual word his-

togram 93% (Diamant et al., 2017) 2017 Cyst (22); Haemangiomas (32); HCC (29); Metastases (35)

11x11 750 Visual word his- togram

sensitivity 83.1%; specificity 93.6%

Table 3.1: Liver lesion classification literature work based on bag-of-visual features.