Segmentation of Diabetic Wound by Multidimensional Clustering for Quantitative Assessment of Healing Process

Babu, K Suri; Subudhi, Asit; Sabut, Sukanta

doi:10.2174/1573405613666161209104534

Cited by 2 publications

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“…The same technique is adopted for measurement of wound image sizes. They stated that the enhanced Fast Fuzzy C-means provides better visual segmented output compared K-means process in terms of high PSNR and low MSE values [5]. The wound image analysis using the Matlab application in the Android smartphone has been implemented successfully.…”

Section: Introductionmentioning

confidence: 99%

Efficient Detection and Classification of Diabetic Foot Ulcer Tissue using PSO Technique

Babu¹,

Sabut²,

Nithya³

2018

IJET

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Assessment of a diabetic wound is very much important to determine the healing status. Foot ulcer is most commonly observed problem of diabetic patients. A diabetic wound is observed for approximately 15 per cent of diabetic patients. Diabetic wound is a major concern of diabetes mellitus. The foot ulcer is the very much harm full problem related to diabetes mellitus. Here particle swarm optimization (PSO) based optimization technique is used for segmentation of diabetic wounds and classifying into three types of tissues i.e. granulation, necrotic and slough. After the segmentation the different textural features are extracted through Gray Level Co-occurrence Matrix (GLCM). All these features were then fed to two different classifiers, Naive bayes and Hoeffding tree for classifying the tissue types. The experimental results showed that the classification accuracy, sensitivity, specificity are 90.90%, 100%, 87.5% by Naive bayes, and 81.81%, 100%, 77.7% by Hoeffding tree respectively. Hence the PSO optimization techniques along with Naive bayes classifier could be used for the effective segmentation of diabetic wound images.

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Section: Introductionmentioning

confidence: 99%

Efficient Detection and Classification of Diabetic Foot Ulcer Tissue using PSO Technique

Babu¹,

Sabut²,

Nithya³

2018

IJET

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Diabetic Wound Segmentation using Convolutional Neural Networks

Cui

Thurnhofer‐Hemsi

Soroushmehr

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Image segmentation is a common goal in many medical applications, as its use can improve diagnostic capability and outcome prediction. In order to assess the wound healing rate in diabetic foot ulcers, some parameters from the wound area are measured. However, heterogeneity of diabetic skin lesions and the noise present in images captured by digital cameras make wound extraction a difficult task. In this work, a Deep Learning based method for accurate segmentation of wound regions is proposed. In the proposed method, input images are first processed to remove artifacts and then fed into a Convolutional Neural Network (CNN), producing a probability map. Finally, the probability maps are processed to extract the wound region. We also address the

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Segmentation of Diabetic Wound by Multidimensional Clustering for Quantitative Assessment of Healing Process

Cited by 2 publications

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Efficient Detection and Classification of Diabetic Foot Ulcer Tissue using PSO Technique

Efficient Detection and Classification of Diabetic Foot Ulcer Tissue using PSO Technique

Diabetic Wound Segmentation using Convolutional Neural Networks

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