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Superpixel Classification with Color and Texture Features for Automated Wound Area Segmentation
Ist Teil von
2018 IEEE Student Conference on Research and Development (SCOReD), 2018, p.1-6
Ort / Verlag
IEEE
Erscheinungsjahr
2018
Link zum Volltext
Quelle
IEEE Electronic Library (IEL)
Beschreibungen/Notizen
Chronic wound is becoming a major threat for world health and economy. In the USA alone, an estimated 6.5 million people are affected by the chronic wound and the annual cost for chronic wound treatment is reportedly more than 25 billion dollars. The process of chronic wound healing is very complex and time-consuming. Quantification of wound size plays a vital role for clinical wound treatment as the physical dimension of a wound is an important clue for wound assessment. The current techniques for wound area measurement are the ruler method and tracing which is mainly based on visual inspection, thus are not very accurate as well as time-consuming. A computerized wound measurement system can provide a more accurate measurement, reduce bias and errors due to fatigue and can potentially reduce clinical workload. In this paper, we proposed a simple but efficient method for wound area segmentation based on superpixel classification with color and texture feature and SVM classifier. Some important findings throughout our experiment are also discussed.