Lung Segmentation using Active Shape Model to Detect the Disease from Chest Radiography

Abstract

BACKGROUND: Some parametric models are used to diagnose problems of lung segmentation more easily and effectively. OBJECTIVE: The present study aims to detect lung diseases (nodules and tuberculosis) better using an active shape model (ASM) from chest radiographs. MATERIAL AND METHODS: In this analytical study, six grouping methods, including three primary methods such as physicians, Dice similarity, and correlation coefficients) and also three secondary methods using SVM (Support Vector Machine) were used to classify the chest radiographs regarding diaphragm congestion and heart reshaping. The most effective method, based on the evaluation of the results by a radiologist, was found and used as input data for segmenting the images by active shape model (ASM). Several segmentation parameters were evaluated to calculate the accuracy of segmentation. This work was conducted on JSRT (Japanese Society of Radiological Technology) database images and tuberculosis database images were used for validation. RESULTS: The results indicated that the ASM can detect 94.12 ± 2.34 and 94.38 ± 3.74 (mean± standard deviation) of pulmonary nodules in left and right lungs, respectively, from the JRST radiology datasets. Furthermore, the ASM model detected 88.33 ± 6.72 and 90.37 ± 5.48 of tuberculosis in left and right lungs, respectively. CONCLUSION: The ASM segmentation method combined with pre-segmentation grouping can be used as a preliminary step to identify areas with tuberculosis or pulmonary nodules. In addition, this presented approach can be used to measure the size and dimensions of the heart in future studies.