Unsupervised techniques of segmentation on texture images: A comparison

Unsupervised Techniques of segmentation are simple and result in satisfactory segmented image. This paper presents an automatic segmentation method based on unsupervised segmentation done on Ultrasound (US) images received from radiologist. US imaging is widely used in clinical diagnosis and image-guided interventions, but suffers from poor quality. One of the most important problems in image processing and analysis is segmentation. US image are difficult to segment due to low contrast and strong speckle noise. Here we present three unsupervised techniques namely thresholding, K-means clustering and expectation maximization and compare their results. Uniqueness of this paper is that EM technique is used on texture featured image which gives far better result of segmentation.