Segmentation of Chronic Wound Areas by Clustering Techniques Using Selected Color Space
This work describes the segmentation of wound areas for chronic wound assessment through appropriate color space selection. For this purpose, wound images grabbed by normal digital camera were preprocessed using combined gray world and retinex method for color correction. Thereafter
a 5 × 5 median filtering and anisotropic diffusion were used for noise reduction and color homogenization respectively. Here fifteen color spaces were compared based on mean contrast between wound and non-wound regions where D
r and D
b chrominance channels
of YD
b
D
r color space were found to provide the highest contrast. Then the wound regions were segmented using clustering techniques viz., k-means and fuzzy c-means on D
r and D
b color channels. The wound segmentation
accuracies were compared which showed 74.39% (k-means) and 72.55% (fuzzy c-means) accuracies in D
r Channel whereas 73.76% (k-means) and 75.23% (fuzzy c-means) in D
b channel. Overall, fuzzy c-means algorithm in D
b
channel provided higher accuracy for wound segmentation.
Keywords: ACCURACY; CHRONIC WOUND IMAGING; COLOR SPACE SELECTION; FUZZY C-MEANS; K-MEANS
Document Type: Research Article
Publication date: 01 March 2013
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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