Soft White Tissue Detection From Pressure Ulcer Images Using Anisotropic Diffused Total Variation Fuzzy C Means

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Raja. S
Ranjith Kumar. R

Abstract

The goal of image segmentation is to cluster pixels into salient image regions. It can identify the regions of interest in an image or annotate the data. In medical imaging, these segments often correspond to different tissue classes, pathologies, or other biologically relevant structures. Medical image segmentation is made difficult by low contrast, noise, and other imaging ambiguities. The goal of segmentation of pressure ulcer images is to find out the level of tissue wound and soft white tissue present. Soft white tissue protein level changes are mostly found in elderly people. Soft white tissue present may be dark red or light yellow gel based on the different imaging modes of severity of pressure ulcer. This helps in diagnosing the disease and to plan for the treatment. The soft white tissue detection is made difficult for the segmentation because of the noise present in the image. Clustering techniques are best suited to segment the input images with noise. Clustering is usually performed when no information is available concerning to the membership of data items to predefined classes. For this reason clustering is traditionally seen as a part of unsupervised learning.

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