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Automated analysis of CXRs is one of the first applications of image analysis in radiology119. Lodwick et al. published the first study on the analysis of pulmonary nodules in chest radiographs120. Since then, research on automated analysis of

1.4 A review of automated tuberculosis detection in chest radiographs 25

CXRs has advanced, and continues to grow further. Past research focused mainly on detection of nodules in CXRs121–123, and limited research was done in other areas like interstitial lung disease, subtraction imaging and pneumothorax124_{. As the focus} of this thesis is TB detection, we will review the existing automated analysis methods relevant for TB detection.

Existing methods for the task of automatic detection of TB are reviewed ex- tensively in Jaeger et al.125_{. Most methods follow the standard CAD pipeline:} preprocessing and segmentation followed by feature extraction and classification. Here, we briefly review the available methods in the literature for which the per- formance numbers were reported. We first review the methods published by our group, followed by the work done by others. van Ginneken was the first one to analyze CXRs automatically for tuberculosis detection in 2001122. He followed an approach where the lung fields were divided into 41 regions, moments of Gaussian were calculated as region features, and k-nearest neighbor classifier was used to estimate the probability of being abnormal per region. The region scores were com- bined to obtain an image level score, using a classification rule which multiplies the probabilities of the regions being normal. The method was tested on a database of 290 normals and 326 abnormals from a TB screening program for people seeking asylum in The Netherlands, and an AUC of 82% was achieved. Arzhaeva et al. applied a completely different approach of multi-valued dissimilarity-based classifi- cation by computing distances to a set of prototype objects as feature vectors, locally on regions inside the lungs and globally on the lung fields126_{. Combining local and} global classification increased the AUC from 81% obtained with global features to 83%, on a set of 217 CXRs (128-normal, 89-abnormal). Hogeweg et al. reported an AUC of 86% on 149 CXRs (69-normal, 80-abnormal) from a TB clinic in Africa, by combining clavicle detection, shape and texture abnormality detection systems127_. Clavicle detection system was used to suppress false responses on clavicles, com- bining various complimentary detection systems is a common approach to improve detection rate and reduce FPs. The most recent work by Hogeweg et al. shows the performance of a combined approach with texture, shape and focal abnormality detection systems128_{. The results have been reported using 10-fold cross-validation} classification on two datasets of 200 CXRs each, collected from the Find and Treat screening program in London, United Kingdom and TB suspect screening popula- tion from Cape Town, Africa. The dataset from London achieved an AUC of 84.7% and 86.8% (87-bacteriological proven TB cases); the dataset from Cape Town got an AUC of 89.9% and 74.1% (66-bacteriological proven TB) against radiological and

26 Introduction

bacteriological reference standard, respectively.

In another approach by Noor et al.129, texture features computed from Daubechies wavelet transform followed by principal component analysis for dimensionality re- duction were used to discriminate between normal and abnormal ROIs in 40 test CXRs (20-normal, 20-bacteriological proven TB). An accuracy of 94% was reported on this small dataset. A semi-automatic method was proposed by Tan et al.130_{, in} which lungs were segmented interactively using a snake model, first-order statistical texture features were extracted on the intensity values, and a decision tree classi- fier in 3-fold classification was trained using 95 CXRs (50-normal, 45-bacteriological proven TB), achieving an AUC of 92.8%. Rijal et al. employed phase congruency features and Euclidean distance measure as classification to differentiate between PTB, healthy tissue and bony structures in regions of 32×32 pixels, and reported an accuracy of 100%, 90% and 50%, respectively131. The method was evaluated on a small dataset of 10 PTB CXRs and 10 normal CXRs. Jeager et al. extracted a set of features, namely LBP, intensity and Hessian shape features from the automati- cally segmented lung fields and trained a linear SVM to distinguish between normal and abnormal X-rays. An AUC of 83.12% was obtained on a dataset from a TB control program in Montgomery County (MC) Maryland, United States, containing 138 CXRs (80-normal, 58-abnormal)132_{. A more recent work from the same author} group used a very large set of features (histograms, shape, curvature, LBP etc.) and applied leave-one-out classification approach, achieving an AUC of 87% and 90% on MC dataset (80-normal, 58-abnormal) and Shenzhen dataset from China (340-normal, 275-abnormal), respectively133_.

The above mentioned CAD systems are mostly based on texture analysis and are not trained to detect all manifestations of TB. Many appearances of TB can not be detected by such systems, namely cavitation, pleural effusion, hilar lymphadenopa- thy, millary TB. A few algorithms in literature specifically focus on detection of a particular manifestation of TB. Shen et al.134 _{proposed a cavity detection system} which analyzes only upper lung zones in CXRs. Initial probable cavity contours were defined by adaptive thresholding and mean shift segmentation followed by an active contour model. These cavity candidate contours were classified as a cavity or a non-cavity using a Bayesian classifier. A second classification step was performed to detect the missed cavities in the upper lobes near clavicles. The technique was tested on only 16 CXRs with cavitation and a threshold on Jaccard overlap measure was used to classify detected cavity regions as true or false positives. A sensitiv- ity of 82.35% at 0.237 FPs per image, and 0.05 per normal image was reported.