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Lung computed axial tomography image segmentation using possibilistic fuzzy C‐means approach for computer aided diagnosis system
Author(s) -
Paulraj Tharcis,
Chelliah Kezi Selva Vijila,
Chinnasamy Sundar
Publication year - 2019
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22340
Subject(s) - fuzzy logic , segmentation , soft computing , computer science , artificial intelligence , field (mathematics) , process (computing) , image segmentation , image (mathematics) , computer aided diagnosis , computed tomography , computer vision , pattern recognition (psychology) , mathematics , radiology , medicine , pure mathematics , operating system
Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C‐means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C‐means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results.