Detection and Classification of Granulation Tissue in Chronic Ulcers

Ahmad Fadzil, Mohd Hani (2011) Detection and Classification of Granulation Tissue in Chronic Ulcers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 7066 L (1). pp. 139-150. ISSN 0302-9743 (Print) 1611-3349 (Online)

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The ability to measure objectively wound healing is important for an effective wound management. Describing wound tissues in terms of percentages of each tissue colour is an approved clinical method of wound assessment. Wound healing is indicated by the growth of the red granulation tissue, which is rich in small blood capillaries that contain haemoglobin pigment reflecting the red colour of the tissue. A novel approach based on utilizing haemoglobin pigment content in chronic ulcers as an image marker to detect the growth of granulation tissue is investigated in this study. Independent Component Analysis is employed to convert colour images of chronic ulcers into images due to haemoglobin pigment only. K-means clustering is implemented to classify and segment regions of granulation tissue from the extracted haemoglobin images. Results obtained indicate an overall accuracy of 96.88% of the algorithm performance when compared to the manual segmentation

Item Type: Article
Impact Factor: SJR (2011): 0.034 SNIP (2011): 0.737
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RL Dermatology
Departments / MOR / COE: Centre of Excellence > Center for Intelligent Signal and Imaging Research
Departments > Electrical & Electronic Engineering
Research Institutes > Institute for Health Analytics
Depositing User: Prof Ir Dr Ahmad Fadzil Mohd Hani
Date Deposited: 12 Dec 2011 07:26
Last Modified: 19 Jan 2017 08:22

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