Open AccessKernel‐based Bayesian clustering of computed tomography images for lung nodule segmentation
Author(s) -
Rajan Baby Yadhu,
Ramayyan Sumathy Vinod Kumar
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5748
Subject(s) - artificial intelligence , thresholding , pattern recognition (psychology) , segmentation , computer science , nodule (geology) , kernel (algebra) , image segmentation , cluster analysis , computer vision , scale space segmentation , bayesian probability , mathematics , image (mathematics) , paleontology , combinatorics , biology
Lung nodule segmentation is an interesting research topic, and it serves as an effective solution for the diagnosis of Lung cancer. The existing methods of lung nodule segmentation suffer from accuracy issues due to the heterogeneity of the nodules in the lungs and the presence of visual deviations in the nodules. Thus, there is a requirement for an effective lung nodule segmentation, which assists the physicians in making accurate decisions. Accordingly, this study proposes a lung nodule segmentation process based on the kernel‐based Bayesian fuzzy clustering (BFC), which is the integration of kernel functions in the BFC. Initially, the input computed tomography image is pre‐processed for ensuring the effective segmentation, and the lobes are identified using the adaptive thresholding strategy. Then, the dominant areas in the lobes are identified using a scale‐invariant feature transform descriptor, and the significant nodules are extracted using the grid‐based segmentation. Finally, the lung nodules are segmented using the proposed kernel‐based BFC. The proposed algorithm is evaluated using the Lung Image Database Consortium and Image Database Resource Initiative database, and it acquires the accuracy, sensitivity, and false positive rate of 0.955, 0.999, and 0.025, respectively.