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. 2014:2014:851582.
doi: 10.1155/2014/851582. Epub 2014 Jul 8.

Automated tissue classification framework for reproducible chronic wound assessment

Rashmi Mukherjee  1 , Dhiraj Dhane Manohar  1 , Dev Kumar Das  1 , Arun Achar  2 , Analava Mitra  1 , Chandan Chakraborty  1
Affiliations

Automated tissue classification framework for reproducible chronic wound assessment

Rashmi Mukherjee et al. Biomed Res Int. 2014.

Abstract

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).

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Figures

Figure 1
Figure 1
Work flow of the proposed computer assisted imaging tissue classification technique.
Figure 2
Figure 2
Photographs of chronic wounds grabbed by a digital camera.
Figure 3
Figure 3
Color conversion: (a-b) original RGB images; (c-d) S component images of (a-b) of HSI.
Figure 4
Figure 4
Neighborhood with different values of radius (R) for calculating LBP.
Figure 5
Figure 5
SVM based data classification.
Figure 6
Figure 6
Segmented results of chronic wound areas using fuzzy divergence based thresholding: (a) original chronic wound images [burn, diabetic ulcer, malignant ulcer, pyoderma gangrenosum, venous ulcer, and pressure ulcer]; (b) saturation (S) component image under HSI color space transformation; (c) segmented wound areas; (d) ground truth marked by the clinician; (e) types of wound tissues (granulation, necrotic, and slough) characterized pseudocolored pixels; (e) representing % of granulation (G), slough (S), and necrotic (N) tissue pixels.
See this image and copyright information in PMC

References

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