Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment
Joint Authors
Chakraborty, Chandan
Achar, Arun
Mitra, Analava
Mukherjee, Rashmi
Manohar, Dhiraj Dhane
Das, Dev Kumar
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-07
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques.
The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast.
Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity.
A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques.
Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images.
The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts.
It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively.
The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793).
American Psychological Association (APA)
Mukherjee, Rashmi& Manohar, Dhiraj Dhane& Das, Dev Kumar& Achar, Arun& Mitra, Analava& Chakraborty, Chandan. 2014. Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment. BioMed Research International،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-503323
Modern Language Association (MLA)
Mukherjee, Rashmi…[et al.]. Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment. BioMed Research International No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-503323
American Medical Association (AMA)
Mukherjee, Rashmi& Manohar, Dhiraj Dhane& Das, Dev Kumar& Achar, Arun& Mitra, Analava& Chakraborty, Chandan. Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-503323
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-503323