Digital image processing
An efficient core-area detection algorithm for fast noise-free image query processing
Proceedings of the 2001 ACM symposium on Applied computing
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
C/C++ Mathematical Algorithms for Scientists and Engineers
C/C++ Mathematical Algorithms for Scientists and Engineers
A System for Segmenting Ultrasound Images
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
IEEE Transactions on Image Processing
Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction
Digital Signal Processing
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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 Hubert 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.