Fuzzy spectral clustering for automated delineation of chronic wound region using digital images

Dhane, Dhiraj Manohar; Maity, Maitreya; Mungle, Tushar; Bar, Chittaranjan; Achar, Arun; Kolekar, Maheshkumar H.; Chakraborty, Chandan

doi:10.1016/j.compbiomed.2017.04.004

Cited by 50 publications

(19 citation statements)

References 38 publications

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“…In their paper, the mean shift algorithm was used to smooth the raw images, and the algorithm of regional growth was applied to segment the images. Dhane et al [9] constructed a similarity matrix. The elements of the matrix are the gray-level fuzzy similarity values combined with the image space information.…”

Section: Related Workmentioning

confidence: 99%

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Wang

Liu

et al. 2018

Computational Intelligence and Neuroscience

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Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

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Section: Related Workmentioning

confidence: 99%

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Wang

Liu

et al. 2018

Computational Intelligence and Neuroscience

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“…There is a growing body of research on diabetic and pressure ulcer applications (45)(46)(47)(48). Thus, far most studies demonstrate methods for improving wound assessments using image recognition (45).…”

Section: Ulcer Assessmentmentioning

confidence: 99%

“…Articles have described applications capable of measuring precise wound boundaries, and differentiating between the types of tissue involved (45)(46)(47)49). For example, Dhane et al demonstrated an AI application's ability to segment the area of ill-defined ulcers with a sensitivity of 87.3% and specificity of 95.7% (47). Mukerjee et al demonstrated an AI application's ability to classify granulation, slough and necrotic tissue with 87.61% accuracy (46).…”

Section: Ulcer Assessmentmentioning

confidence: 99%

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

et al. 2020

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“…However, this method often contained many parameters that required manual adjustment for different images. Moreover, as we target user-generated incision images that come from amateur imaging devices (e.g., hand-held mobile phones), we expect image quality to be both decreased and variable [ 55 ]. In addition to the diverse incision characteristics, the imprecise definition of incision boundaries also complicates the problem.…”

Section: Challenges In User-generated Incision Imagesmentioning

confidence: 99%

“…In this article, we propose a roadmap for developing incision image algorithms for automatic SSI detection and evaluation. Challenges persist, ranging from limited photo quality and uncontrolled imaging variations (e.g., light and angle), to the enormous heterogeneity of patients that calls for personalization in our algorithms [ 55 ]. We introduce both novelty and challenges in using incision images for SSI detection and evaluation and provide an overview of recent and related developments in computer vision, medical imaging processing, and analysis.…”

mentioning

confidence: 99%

A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images

Jiang

Ardywibowo

Samereh

et al. 2019

Surgical Infections

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Background: Emerging technologies such as smartphones and wearable sensors have enabled the paradigm shift to new patient-centered healthcare, together with recent mobile health (mHealth) app development. One such promising healthcare app is incision monitoring based on patient-taken incision images. In this review, challenges and potential solution strategies are investigated for surgical site infection (SSI) detection and evaluation using surgical site images taken at home. Methods: Potential image quality issues, feature extraction, and surgical site image analysis challenges are discussed. Recent image analysis and machine learning solutions are reviewed to extract meaningful representations as image markers for incision monitoring. Discussions on opportunities and challenges of applying these methods to derive accurate SSI prediction are provided. Conclusions: Interactive image acquisition as well as customized image analysis and machine learning methods for SSI monitoring will play critical roles in developing sustainable mHealth apps to achieve the expected outcomes of patient-taken incision images for effective out-of-clinic patient-centered healthcare with substantially reduced cost.

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Fuzzy spectral clustering for automated delineation of chronic wound region using digital images

Cited by 50 publications

References 38 publications

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images

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