NDLI: Bacterial foraging based edge detection for cell image segmentation

Content Provider	IEEE Xplore Digital Library
Author	Yongsheng Pan Tao Zhou Yong Xia
Copyright Year	2015
Description	Author affiliation: Sch. of Sci., Ningxia Med. Univ., Yinchuan, China (Tao Zhou) \|\| Shaanxi Key Lab. of Speech & Image Inf. Process., Northwestern Polytech. Univ., Xi'an, China (Yongsheng Pan; Yong Xia)
Abstract	Edge detection is the most popular and common choices for cell image segmentation, in which local searching strategies are commonly used. In spite of their computational efficiency, traditional edge detectors, however, may either produce discontinued edges or rely heavily on initializations. In this paper, we propose a bacterial foraging based edge detection (BFED) algorithm for cell image segmentation. We model the gradients of intensities as the nutrient concentration and propel bacteria to forage along nutrient-rich locations via mimicking the behavior of Escherichia coli, including the chemotaxis, swarming, reproduction, elimination and dispersal. As a nature-inspired evolutionary technique, this algorithm can identify the desired edges and mark them as the tracks of bacteria. We have evaluated the proposed algorithm against the Canny, SUSAN, Verma's and an active contour model (ACM) based edge detectors on both synthetic and real cell images. Our results suggest that the BFED algorithm can identify boundaries more effectively and provide more accurate cell image segmentation.
Starting Page	3873
Ending Page	3876
File Size	873256
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319239
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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

Segmentation of cell clumps for quantitative analysis

Image segmentation for enhancing symbol recognition in prosthetic vision

Quantifying tissue heterogeneity using quadtree decomposition

Medical Image Segmentation Using Feature-based GVF Snake

Integral invariants for image enhancement

Employing temporal information for cell segmentation using max-flow/min-cut in phase-contrast video microscopy

An improved gradient vector flow algorithm for optic disc segmentation

Automatic nuclei segmentation and spatial FISH analysis for cancer detection

A graph-theoretic approach for segmentation of PET images

Bacterial foraging based edge detection for cell image segmentation

Similar Documents

Segmentation of cell clumps for quantitative analysis

Image segmentation for enhancing symbol recognition in prosthetic vision

Quantifying tissue heterogeneity using quadtree decomposition

Medical Image Segmentation Using Feature-based GVF Snake

Integral invariants for image enhancement

Employing temporal information for cell segmentation using max-flow/min-cut in phase-contrast video microscopy

An improved gradient vector flow algorithm for optic disc segmentation

Automatic nuclei segmentation and spatial FISH analysis for cancer detection

A graph-theoretic approach for segmentation of PET images

Bacterial foraging based edge detection for cell image segmentation