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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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Work flow of the proposed computer assisted imaging tissue classification technique.
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fig1: Work flow of the proposed computer assisted imaging tissue classification technique.

Mentions: In India, very few studies have been performed though risk factors of CW like diabetes, atherosclerosis, tuberculosis, leprosy, and trauma are very much prevalent. Nayak et al. addressed the composition of different types of tissue based on color and pigmentation inside the wound by image processing [16]. Extensive literature survey revealed that there is an urgent requirement for quantitative estimation of wound tissue classification within the wound bed, which might assist clinicians to effectively monitor the wound healing rate. In view of this, we have proposed here a computer assisted tissue classification methodology using fuzzy divergence based CW region segmentation and statistical machine learning techniques. The overall workflow has been depicted in Figure 1.


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)

Work flow of the proposed computer assisted imaging tissue classification technique.
© Copyright Policy
Related In: Results  -  Collection

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

fig1: Work flow of the proposed computer assisted imaging tissue classification technique.
Mentions: In India, very few studies have been performed though risk factors of CW like diabetes, atherosclerosis, tuberculosis, leprosy, and trauma are very much prevalent. Nayak et al. addressed the composition of different types of tissue based on color and pigmentation inside the wound by image processing [16]. Extensive literature survey revealed that there is an urgent requirement for quantitative estimation of wound tissue classification within the wound bed, which might assist clinicians to effectively monitor the wound healing rate. In view of this, we have proposed here a computer assisted tissue classification methodology using fuzzy divergence based CW region segmentation and statistical machine learning techniques. The overall workflow has been depicted in Figure 1.

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