Skin wound analysis using digital image processing

Oduncu, Hakan; Aslanta, V.; Tunckanat, Mehmet; Kurban, Rıfat

doi:10.1109/siu.2005.1567767

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…in [6]) or semi-automatic (e.g. in [7]) techniques. Following is usually the image pre-processing step for color correction and white balance estimation (e.g.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

Nejati

Ghazijahani

Abdollahzadeh

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Tissue assessment for chronic wounds is the basis of wound grading and selection of treatment approaches. While several image processing approaches have been proposed for automatic wound tissue analysis, there has been a shortcoming in these approaches for clinical practices. In particular, seemingly, all previous approaches have assumed only 3 tissue types in the chronic wounds, while these wounds commonly exhibit 7 distinct tissue types that presence of each one changes the treatment procedure. In this paper, for the first time, we investigate the classification of 7 wound tissue types. We work with wound professionals to build a new database of 7 types of wound tissue. We propose to use pre-trained deep neural networks for feature extraction and classification at the patch-level. We perform experiments to demonstrate that our approach outperforms other state-of-the-art. We will make our database publicly available to facilitate research in wound assessment.

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“…in [6]) or semi-automatic (e.g. in [7]) techniques. Following is usually the image pre-processing step for color correction and white balance estimation (e.g.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

Nejati

Ghazijahani

Abdollahzadeh

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Differential Evolution Algorithm For Segmentation Of Wound Images

Aslantaş

Tunckanat

2007

2007 IEEE International Symposium on Intelligent Signal Processing

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A Novel Threshold Technique and Fuzzy C-Means Algorithm for Segmentation of Wound

Konakala¹,

Putheti²

2013

IJCA

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Skin color based wound region segmentation is proposed by using threshold segmentation and fuzzy-C means techniques. This letter gives wound on skin location should be precisely identified. These methods are identified the edge regions of the wound part that can help clinicians in their diagnosis. This letter describes the use of threshold values and FCM clustering algorithms for detection of wounds on the skin. In this letter This paper presents the application of fuzzy Cmeans clustering algorithm for skin color segmentation problem. This proposed method is able to take both the distributions of color space and the spatial interaction between surrounding pixels during clustering. This clustering algorithm have more advantages the result obtained from different wound images have been discussed.

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Skin wound analysis using digital image processing

Cited by 3 publications

References 12 publications

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

Differential Evolution Algorithm For Segmentation Of Wound Images

A Novel Threshold Technique and Fuzzy C-Means Algorithm for Segmentation of Wound

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