TY - JOUR ID - 47954 TI - Lung Segmentation using Active Shape Model to Detect the Disease from Chest Radiography JO - Journal of Biomedical Physics and Engineering JA - JBPE LA - en SN - AU - Dorri Giv, Masoumeh AU - Haghighi Borujeini, Meysam AU - Seifi Makrani, Danial AU - Dastranj, Leila AU - Yadollahi, Masoumeh AU - Semyari, Somayeh AU - Sadrnia, Masoud AU - Ataei, Gholamreza AU - Riahi Madvar, Hamideh AD - PhD, Nuclear Medicine Research Center, Department of Nuclear Medicine, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran AD - MSc, Department of Medical Physics, Isfahan University of Medical Sciences, Isfahan, Iran AD - PhD Candidate, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran AD - MSc, Department of Physics, Hakim Sabzevari Universuty, Sabzevar, Iran AD - MSc, Department of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran AD - MSc, Department of Physic, Imam Khomeini International University, Qazvin, Iran AD - BSc, Department of Radiology Technology, Rofeideh Rehabilitation Hospital, Tehran, Iran AD - MSc, Department of Radiology Technology, Faculty of Paramedical Sciences, Babol University of Medical Science, Babol, Iran AD - MSc, Department of Nuclear Engineering, Faculty of Engineering, Science and Research of Tehran Branch, Islamic Azad University, Tehran, Iran Y1 - 2021 PY - 2021 VL - 11 IS - 6 SP - 747 EP - 756 KW - Active Shape Model KW - Lung Diseases KW - Segmentation KW - Chest KW - Heart KW - Diaphragm Radiograph KW - Radiography DO - 10.31661/jbpe.v0i0.2105-1346 N2 - 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. UR - https://jbpe.sums.ac.ir/article_47954.html L1 - https://jbpe.sums.ac.ir/article_47954_161a03569c3b8c1215ce8bc0b3272a72.pdf ER -