Robust image segmentation by texture sensitive snake under low contrast environment

Abstract

Robust image segmentation plays an important role in a wide range of daily applications, like visual surveillance system, computer-aided medical diagnosis, etc. Although commonly used image segmentation methods based on pixel intensity and texture can help finding the boundary of targets with sharp edges or distinguished textures, they may not be applied to images with poor quality and low contrast. Medical images, images captured from web cam and images taken under dim light are examples of images with low contrast and with heavy noise. To handle these types of images, we proposed a new segmentation method based on texture clustering and snake fitting. Experimental results show that targets in both artificial images and medical images, which are of low contrast and heavy noise, can be segmented from the background accurately. This segmentation method provides alternatives to the users so that they can keep using imaging device with low quality outputs while having good quality of image analysis result.