Lung Segmentation using Active Shape Model to Detect the Disease from Chest Radiography
نویسندگان
1 PhD, Nuclear Medicine Research Center, Department of Nuclear Medicine, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
2 MSc, Department of Physics, Hakim Sabzevari Universuty, Sabzevar, Iran
3 MSc, Department of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran
4 MSc, Department of Physic, Imam Khomeini International University, Qazvin, Iran
5 MSc, Department of Nuclear Engineering, Faculty of Engineering, Science and Research of Tehran Branch, Islamic Azad University, Tehran, Iran
6 MSc, Department of Medical Physics, Isfahan University of Medical Sciences, Isfahan, Iran
7 PhD Candidate, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
8 BSc, Department of Radiology Technology, Rofeideh Rehabilitation Hospital, Tehran, Iran
9 MSc, Department of Radiology Technology, Faculty of Paramedical Sciences, Babol University of Medical Science, Babol, Iran
doi
10.31661/jbpe.v0i0.2105-1346چکیده
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.
