Texture-based detection of lung pathology in chest radiographs using local binary patterns

• Main Authors: Melendez, Gil Paulo, Cordel, Macario
• Format: text
• Published: Animo Repository 2016
• Subjects: Heart—Hypertrophy; Heart—Dilatation; Chest—Radiography; Diagnosis—Data processing; Support vector machines; Binary system (Mathematics); Analytical, Diagnostic and Therapeutic Techniques and Equipment; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2513; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3512/type/native/viewcontent
• Institution: De La Salle University

This paper presents a method that employs texture-based feature extraction and Support Vector Machines (SVM) to classify chest abnormal radiograph patterns namely pleural effusion, pnuemothorax, cardiomegaly and hyperaeration. A similar previous attempt prototyped the classification system that achieved 97% and 87.5% accuracy for pleural effusion and pneumothorax using histogram values, while attaining 70% and 73.33% for cardiomegaly and hyperaeration using image processing schemes. In this work, we aimed to increase the performance in classifying the said lung patterns, specifically for cardiomegaly and hyperaeration. Using texture-based features, the developed system was able to achieve accuracies of 96% and 99% with sensitivities of 97% and 100% for the cardiomegaly and hyperaeration cases, respectively. © 2015 IEEE.