Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme. (15th November 2020) - Chandra, Tej Bahadur; Verma, Kesari; Singh, Bikesh Kumar; Jain, Deepak; Netam, Satyabhuwan Singh
Highlights: Developed a hierarchical method to mimic radiologist's interpretation procedure. Proposed seventeen geometrical shape features to encode thoracic abnormalities. Combined shape features with texture features for improved abnormality detection. Disease detection performance improved significantly using combined feature set. The statistical evaluation proved the significance of the obtained results. Abstract: Machine learning techniques have been widely used for abnormality detection in medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools used to detect various disease pathologies. The ambiguous anatomical structure of soft tissues is one of the major challenges for segregating normal and abnormal images. The main objective of this study is to mimic the expert radiologist's interpretation procedure in computer-aided diagnosis (CAD) systems. We propose an automatic technique for detection of abnormal CXR images containing one or more pathologies like pleural effusion, infiltration, fibrosis, hila enlargement, dense consolidation, etc. due to tuberculosis (TB). The proposed abnormality detection technique is based on the hierarchical feature extraction scheme in which the features are used in two-level of hierarchy to categorize healthy and unhealthy groups. In level one the handcrafted geometrical features like shape, size, eccentricity, perimeter, etc. and in level 2 traditional first order statistical feature along with texture features like energy, entropy, contrast, correlation, etc. are extracted from segmented lung-fields. Further, a supervised classification approach is employed on the extracted features to detect normal and abnormal CXR images. The performance of the algorithm is validated on a total of 800 CXR images from two public datasets, namely the Montgomery set and Shenzhen set. The obtained results (accuracy = 95.60 ± 5.07% and area under curve (AUC) = 0.95 ± 0.06 for Montgomery collection, and accuracy = 99.40 ± 1.05% and AUC = 0.99 ± 0.01 for Shenzhen collection) shows the promising performance of the proposed technique for TB detection compared to the existing state of the art approaches. Further, the obtained results are statistically validated using Friedman post-hoc multiple comparison methods, which confirms the significance of the proposed method.