Role of Gist and PHOG features in computer-aided diagnosis of tuberculosis without segmentation.

Arun Chauhan,Chittaranjan Rout,Devesh Chauhan,Hans A Kestler

doi:10.1371/journal.pone.0112980

Abstract

PurposeEffective diagnosis of tuberculosis (TB) relies on accurate interpretation of radiological patterns found in a chest radiograph (CXR). Lack of skilled radiologists and other resources, especially in developing countries, hinders its efficient diagnosis. Computer-aided diagnosis (CAD) methods provide second opinion to the radiologists for their findings and thereby assist in better diagnosis of cancer and other diseases including TB. However, existing CAD methods for TB are based on the extraction of textural features from manually or semi-automatically segmented CXRs. These methods are prone to errors and cannot be implemented in X-ray machines for automated classification.MethodsGabor, Gist, histogram of oriented gradients (HOG), and pyramid histogram of oriented gradients (PHOG) features extracted from the whole image can be implemented into existing X-ray machines to discriminate between TB and non-TB CXRs in an automated manner. Localized features were extracted for the above methods using various parameters, such as frequency range, blocks and region of interest. The performance of these features was evaluated against textural features. Two digital CXR image datasets (8-bit DA and 14-bit DB) were used for evaluating the performance of these features.ResultsGist (accuracy 94.2% for DA, 86.0% for DB) and PHOG (accuracy 92.3% for DA, 92.0% for DB) features provided better results for both the datasets. These features were implemented to develop a MATLAB toolbox, TB-Xpredict, which is freely available for academic use at http://sourceforge.net/projects/tbxpredict/. This toolbox provides both automated training and prediction modules and does not require expertise in image processing for operation.ConclusionSince the features used in TB-Xpredict do not require segmentation, the toolbox can easily be implemented in X-ray machines. This toolbox can effectively be used for the mass screening of TB in high-burden areas with improved efficiency.

Highlights

X-rays were discovered by Wilhelm Rontgen, a German physicist in 1895 and have revolutionized the field of diagnostics
Prediction accuracy of only 65.4% was obtained for grey-level cooccurrence matrix (GLCM) textural features extracted from automatically segmented CXRs, while 80.8% was obtained for manually segmented CXRs from dataset Dataset A (DA)
computer-aided diagnosis (CAD) techniques provide second opinion to the clinicians for their findings, so its implementation is expected to improve their performance in the diagnosis of TB

Summary

Introduction

X-rays were discovered by Wilhelm Rontgen, a German physicist in 1895 and have revolutionized the field of diagnostics. Chest radiographs (CXRs) are ubiquitous in clinical diagnostics and make up about one-third of all radiological examinations. Chest radiographs are inexpensive and least invasive primary diagnostic tool for tuberculosis (TB). They are used in routine medical checkups and immigrant medical examinations even in well-equipped hospitals where blood and skin tests are available [1]. The disease is diagnosed on the basis of patient’s symptoms, CXR and smear microscopy tests. Since the accuracy of smear microscopy test has been less than 50%, diagnosis relies primarily on the interpretation of radiological patterns found in a CXR. Computer-aided diagnosis (CAD) tools assume a lot of significance as they reduce diagnostic errors and increase the efficiency of mass screening in poor-resource settings

Methods

Results

Conclusion