Segmentation of ultrasound liver images: An automatic approach

Segmentation of ultrasound liver images presents a unique challenge because these images contain strong speckle noise and attenuated artifacts. Most ultrasound image segmentation techniques focus on region growing or active contours. These are semi-automatic segmenting systems, in which seed points or initial contours have to be manually identified. In this paper, we propose a fully automatic segmentation system for ultrasound liver images. We apply the Peak-and-valley method to pixels scanned along the Hilbert curve, and propose a "windows adaptive threshold" procedure to further reduce noise from the images. After Otsu's segmentation algorithm is applied to the images, a core area algorithm is employed to detect liver objects with the help of a feature knowledge base. We compared our method with other techniques and the manual segmentation method. The results indicate the accuracy of our system and our automatically segmented images contain less noise than the other methods.