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Automated tissue classification framework for reproducible chronic wound assessment.

Mukherjee R, Manohar DD, Das DK, Achar A, Mitra A, Chakraborty C - Biomed Res Int (2014)

Bottom Line: A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques.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).

View Article: PubMed Central - PubMed

Affiliation: School of Medical Science & Technology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India.

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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Color conversion: (a-b) original RGB images; (c-d) S component images of (a-b) of HSI.
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fig3: Color conversion: (a-b) original RGB images; (c-d) S component images of (a-b) of HSI.

Mentions: In order to segment the boundary of wound bed, filtered RGB wound image was converted into HSI (H: hue, S: saturation, I: intensity) color space as it is more close to the way humans perceive the color. In fact, H describes pure color where S provides the degree to which a pure color is diluted by white light and I is subjective color [20]. In order to avoid any color conflict during segmentation of wound area from skin, only S component of HSI channels was selected here that showed the improved contrast at the wound boundary as shown in Figure 3.


Automated tissue classification framework for reproducible chronic wound assessment.

Mukherjee R, Manohar DD, Das DK, Achar A, Mitra A, Chakraborty C - Biomed Res Int (2014)

Color conversion: (a-b) original RGB images; (c-d) S component images of (a-b) of HSI.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4121018&req=5

fig3: Color conversion: (a-b) original RGB images; (c-d) S component images of (a-b) of HSI.
Mentions: In order to segment the boundary of wound bed, filtered RGB wound image was converted into HSI (H: hue, S: saturation, I: intensity) color space as it is more close to the way humans perceive the color. In fact, H describes pure color where S provides the degree to which a pure color is diluted by white light and I is subjective color [20]. In order to avoid any color conflict during segmentation of wound area from skin, only S component of HSI channels was selected here that showed the improved contrast at the wound boundary as shown in Figure 3.

Bottom Line: A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques.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).

View Article: PubMed Central - PubMed

Affiliation: School of Medical Science & Technology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India.

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).

Show MeSH
Related in: MedlinePlus