Automated Lung Segmentation on Computed Tomography Image for the Diagnosis of Lung Cancer.

Ratishchandra Huidrom, Yambem Jina Chanu, Khumanthem Manglem Singh


Image processing techniques are widely used in several medical areas for early detection and treatment especially in the detection of various cancer tumors such as Squamous, Adenocarcinoma, Large Cell Carcinomas and Small Cell Lung Cancer. Segmentation of lung tissues from Computed Tomography (CT) image is considered as a pre-processing step in Lung Imaging. However, during Lung Segmentation, the Juxta-Pleural nodules (nodules attach to parenchymal walls) are missed out as they have similar appearance (intensity) to that of other non-pulmonary structures, which leads to a challenge to segment lung region along with Juxta-Pleural nodules. The complexity to segment lung region is mainly due to its inhomogeneity (different structures and intensity values of lungs). Thus, the existing segmentation algorithms like image thresholding algorithm, region-growing algorithm, active contour, level sets, etc. fail to segment lung tissues including Juxta-Pleural nodules. So, in this paper, a new fully-automated lung segmentation method with Juxta-Pleural nodules inclusion, is proposed.


Computed Tomography, Image Processing, Juxta-Pleural nodules, Lung Segmentation, Image Thresholding, Lung Imaging

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