NDLI: Kernel-based Bayesian clustering of computed tomography images for lung nodule segmentation

Content Provider	IET Digital Library
Author	Baby, Yadhu Rajan Sumathy, Vinod Kumar Ramayyan
Abstract	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.
Starting Page	890
Ending Page	900
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 5, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5748
Journal	IET Image Processing
Publisher Date	2019-12-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Thresholding Strategy Algebra Bayes Method Biology And Medical Computing Biomedical Imaging/measurement Cancer Combinatorial Mathematics Computer Vision And Image Processing Technique Computerised Tomography Effective Lung Nodule Segmentation Effective Segmentation False Positive Rate Feature Extraction Fuzzy Set Theory Graph Theory Grid-based Segmentation Image Database Resource Initiative Database Image Recognition Image Segmentation Input Computed Tomography Image Kernel Function Kernel-based Bayesian Fuzzy Clustering Kernel-based BFC Lung Lung Cancer Diagnosis Lung Image Database Consortium Lung Nodule Segmentation Process Medical Image Processing Patient Diagnostic Method And Instrumentation Pattern Clustering Probability Theory Radiography And Computed Tomography Scale-invariant Feature Transform Descriptor Set Theory Statistics Stochastic Linearised SCUC X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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