Dorri Giv Masoumeh, Haghighi Borujeini Meysam, Seifi Makrani Danial, Dastranj Leila, Yadollahi Masoumeh, Semyari Somayeh, Sadrnia Masoud, Ataei Gholamreza, Riahi Madvar Hamideh
PhD, Nuclear Medicine Research Center, Department of Nuclear Medicine, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran.
MSc, Department of Medical Physics, Isfahan University of Medical Sciences, Isfahan, Iran.
J Biomed Phys Eng. 2021 Dec 1;11(6):747-756. doi: 10.31661/jbpe.v0i0.2105-1346. eCollection 2021 Dec.
Some parametric models are used to diagnose problems of lung segmentation more easily and effectively.
The present study aims to detect lung diseases (nodules and tuberculosis) better using an active shape model (ASM) from chest radiographs.
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.
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.
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.
一些参数模型用于更轻松、有效地诊断肺部分割问题。
本研究旨在使用胸部X光片的主动形状模型（ASM）更好地检测肺部疾病（结节和肺结核）。
在这项分析研究中，使用了六种分组方法，包括三种主要方法（如医生诊断、骰子相似性和相关系数）以及三种使用支持向量机（SVM）的次要方法，对胸部X光片进行膈肌充血和心脏重塑方面的分类。根据放射科医生对结果的评估，找出最有效的方法，并将其用作主动形状模型（ASM）分割图像的输入数据。评估了几个分割参数以计算分割的准确性。这项工作在JSRT（日本放射技术学会）数据库图像上进行，并使用肺结核数据库图像进行验证。
结果表明，ASM分别从JRST放射学数据集中检测出左肺和右肺中94.12±2.34%和94.38±3.74%（平均值±标准差）的肺结节。此外，ASM模型分别检测出左肺和右肺中88.33±6.72%和90.37±5.48%的肺结核。
ASM分割方法与预分割分组相结合可作为识别肺结核或肺结节区域的初步步骤。此外，这种提出的方法可用于未来研究中测量心脏的大小和尺寸。
