Automated pathological lung volume segmentation with anterior and posterior separation in X-ray CT images Online publication date: Thu, 05-Jan-2023
by Anita Khanna; Narendra Londhe; Shubhrata Gupta
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 40, No. 4, 2022
Abstract: 3D volume lung segmentation is a precursor for morphometric and volumetric analysis. The proposed work is a fully automated lung segmentation method with due attention given to left and right lung separation in the anterior and posterior sections involving new concept of bounding box. The method proceeds in three steps: firstly, lung segmentation performed with morphological operations. Secondly, airways extracted using 3D region growing. Finally, left and right lung lobes separated by analysing bounding box characteristics of each image. The performance matrices and net volume of lung have been evaluated with manual analysis and the results are quite satisfactory with average F1 score 0.983, precision 0.989, recall 0.976, specificity 0.998 and Jaccard index 0.965 and comparative lung volumes. The proposed method showed the consistency with reliability index of 97.72%. The time taken for complete segmentation for each subject is between 60-70 sec on Intel Core i7-8750H, CPU @ 2.20 GHz.
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