Please use this identifier to cite or link to this item: http://monografias.ufrn.br/handle/123456789/7677
Title: Ulcer Segmentation and Tissue Classification using Color Texture Clustering
Authors: Marques, Vítor de Godeiro
Keywords: Larval therapy;Chronic wounds;Image segmentation;Tissue classification;Color image analysis;Clustering
Issue Date: 23-Nov-2018
Publisher: Universidade Federal do Rio Grande do Norte
Citation: Marques, Vítor de Godeiro. Ulcer segmentation and tissue classification using color texture clustering. 85f. Monografia (Bacharelado em Ciência da Computação) - Departamento de Informática e Matemática Aplicada Universidade Federal do Rio Grande do Norte. Natal, 2018.
Portuguese Abstract: Chronic Wounds are ulcers presenting a difficult or nearly interrupted cicatrization process that increases the risk of complications to the health of patients, like amputations and infections. This research proposes a general noninvasive methodology for the segmentation and analysis of images of chronic wounds by computing the wound areas affected by necrosis, as opposed to invasive techniques that are commonly used for this calculation, such as manual planimetry with plastic films. We investigated algorithms to perform the segmentation of wounds and classification of tissues as Necrotic, Granulation or Slough. In the proposed methodology, we used histogram based textural descriptions, that were compared by using the Earth Mover's Distance, and proposed a color space reduction methodology that increased the reported accuracies, specificities, sensitivities and Dice coefficients. We also developed a mobile app prototype to show that it is possible to employ such application for supporting Larval Therapy on mobile devices.
URI: http://monografias.ufrn.br/jspui/handle/123456789/7677
Other Identifiers: 20180008316
Appears in Collections:Ciência da Computação

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