Ulcer Segmentation and Tissue Classification using Color Texture Clustering

• Autor(a) principal: Marques, Vítor de Godeiro
• Data de Publicação: 2018
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/handle/123456789/34203
• topic: Larval therapy; Chronic wounds; Image segmentation; Tissue classification; Color image analysis; Clustering

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.