Analysis of Chronic Wound Images Using Factorization Based Segmentation and Machine Learning Methods-Reference-Cited by-同舟云学术

Analysis of Chronic Wound Images Using Factorization Based Segmentation and Machine Learning Methods

Published:2017 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics - ICCBB 2017
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Author:

Kavitha I.¹,Suganthi S. S.²,Ramakrishnan S.¹

Affiliation:

1. Indian Institute of Technology Madras, Chennai, India

2. Tata Elxsi Limited, IITM Research Park, Chennai, India

Publisher

ACM Press

Reference15 articles.

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2. L. M. Morton and T. J. Philips. 2016. Wound healing and treating wounds Differential diagnosis and evaluation of chronic wounds. J. Am. Dermatology, 74, 4 (April 2016), 589--605. DOI= https://www.ncbi.nlm.nih.gov/pubmed/26979352

3. A. C. Chang, B. Dearman, and J. E. Greenwood. 2011. A comparison of wound area measurement techniques: visitrak versus photography. Eplasty. 11, e18 (April 2011), 158--166. DOI= https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3080766/

4. D. M. Dhane, V. Krishna, A. Achar, and C. Bar. 2016. Spectral Clustering for Unsupervised Segmentation of Lower Extremity Wound Beds Using Optical Images. J. Med. Syst, 40, 207 (August 2016), 1--10. DOI= https://www.ncbi.nlm.nih.gov/pubmed/27520612

5. M. K. Yadav and D. D. Manohar. 2016. Segmentation of Chronic Wound Areas by Clustering Techniques Using Selected Color Space Segmentation of Chronic Wound Areas by Clustering Techniques Using Selected Color Space. J. Med. Imaging Heal. Informatics, 3, 1 (Nov. 2016), 22--29. DOI= http://www.ingentaconnect.com/content/asp/jmihi/2013/00000003/00000001/art00005?crawler=true

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