Automated liver detection in ultrasound images

To detect the right position of liver objects in ultrasound image is a critical issue in medical image analysis and visualization. Most ultrasound image segmentation techniques focus on region growing and Active contours. These are semi-automatic segmenting systems because these approaches need a user to identify a seed point or to draw an initial contour. This paper proposes a novel automatic segmenting system to detect liver in ultrasound images. The peak-and-valley is adapted by scanning pixel along with the Hilbert curve. A “local adaptive threshold” procedure is proposed to further reduce noise from the images. After Otsu 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. The proposed method is compared with other techniques and the manual segmentation method. The results indicate the accuracy of the proposed system and these automatically segmented images contain less noise than the other methods. This system supports automated liver detection in ultrasound images.