Wavelet-based segmentation for fetal ultrasound texture images

Abstract

This paper introduces an efficient algorithm for segmentation of fetal ultrasound images using the multiresolution analysis technique. The proposed algorithm decomposes the input image into a multiresolution space using the packet two-dimensional wavelet transform. The system builds features vector for each pixel that contains information about the gray level, moments and other texture information. These vectors are used as inputs for the fuzzy c-means clustering method, which results in a segmented image whose regions are distinct from each other according to texture characteristic content. An Adaptive Center Weighted Median filter is used to enhance fetal ultrasound images before wavelet decomposition. Experiments indicate that this method can be applied with promising results. Preliminary experiments indicate good results in image segmentation while further studies are needed to investigate the potential of wavelet analysis and fuzzy c-means clustering methods as a tool for detecting fetus organs in digital ultrasound images.