Superpixel Classification with Color and Texture Features for Automated Wound Area Segmentation


Abas, Fazly Salleh and Ahmad Fauzi, Mohammad Faizal and Biswas, Topu and Nair, Harikrishna K. R. (2019) Superpixel Classification with Color and Texture Features for Automated Wound Area Segmentation. In: 16th IEEE Student Conference on Research and Development, SCOReD 2018, 26 -28 November 2018, Selangor, Malaysia.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Image segmentation
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions: Faculty of Engineering (FOE)
Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 26 Jan 2022 03:14
Last Modified: 26 Jan 2022 03:14


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