NDLI: A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation: An efficient variants of fuzzy clustering technique with spatial information for effective noisy medical image segmentation

Content Provider	IEEE Xplore Digital Library
Author	Beevi, S.Z. Sathik, M.M. Senthamaraikannan, K. Yasmin, J.H.J.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, Sathakathullah Appa College, Tamilnadu, India (Sathik, M.M.) \|\| Department of IT, National College of Engineering, Tamilnadu, India (Beevi, S.Z.) \|\| Department of Statistics, Manonmaniam Sundaranar University, Tamilnadu, India (Senthamaraikannan, K.) \|\| Department of Computer Science, National College of Engineering, Tamilnadu, Inida (Yasmin, J.H.J.)
Abstract	Segmentation is an important step in many medical imaging applications and a variety of image segmentation techniques do exist. Of them, a group of segmentation algorithms is based on the clustering concepts. In our research, we have intended to devise efficient variants of Fuzzy C-Means (FCM) clustering towards effective segmentation of medical images. The enhanced variants of FCM clustering are to be devised in a way to effectively segment noisy medical images. The medical images generally are bound to contain noise while acquisition. So, the algorithms devised for medical image segmentation must be robust to noise for achieving desirable segmentation results. The existing variants of FCM-based algorithms, segment images without considering the spatial information, which makes it sensitive to noise. We proposed the algorithm, which incorporate spatial information into FCM, have shown considerable resilience to noise, yet with increased noise levels in images, these approaches have not performed exceptionally well. In the proposed research, the input noisy medical images are employed to a denoising algorithm with the help of effective denoising algorithm prior to segmentation. Moreover, the proposed approach will improve upon the existing variants of FCM-based segmentation algorithms by integrating the spatial neighborhood information present in the images for better segmentation. The spatial neighborhood information of the images will be determined using a factor that represents the spatial influence of the neighboring pixels on the current pixel. The employed factor works on the assumption that the membership degree of a pixel to a cluster is greatly influenced by the membership of its neighborhood pixels. Subsequently, the denoised images will be segmented using the designed variants of FCM. The proposed segmentation approach will be robust to noisy images even at increased levels of noise, thereby enabling effective segmentation of noisy medical images.
Starting Page	1
Ending Page	8
File Size	1391253
Page Count	8
File Format	PDF
ISBN	9781424465910
e-ISBN	9781424465927
DOI	10.1109/ICCCNT.2010.5591787
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Histograms Variants Membership function Fuzzy C-means Noise Noise reduction Clustering algorithms Clustering Pixel Biomedical imaging
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in