In this paper, the self organization properties of genetic algorithms are employed to tackle the problem of feature selection and extraction in ultrasound images, which can facilitate early disease detection and diagnosis. Accurately identifying the aberrant features at a particular location of clinical ultrasound images is important to find the possibly damaged tissues. Unfortunately, it is difficult to exactly detect the regions of interest (ROIs) from relatively low quality of clinical ultrasound images. The presented evolutionary optimization algorithm presents a novel approach to building features for automatic liver cirrhosis diagnosis using a genetic algorithm. The extracted features provide several advantages over other feature extraction techniques which include: automatically construct feature set and tune their parameters, ability to integrate multiple feature sets to improve the diagnosis accuracy, and ability to find local ROIs and integrate their local features into effective global features. As compared with past approaches, we span a new way to unify the processing steps in a clinical application using the evolutionary optimization algorithms for ultrasound images. Experimental results show the effectiveness of the proposed method.

A new color image compression technique based on moment-preserving and block truncation coding is proposed. An input image is divided into nonoverlapping blocks and each block pixel is assigned one of two representative colors, which are computed with analytic formulas derived from preserving certain moments in the block. A bit map is then generated for each block to represent the pixels' colors. Different uniformity conditions in the representative colors are also identified and utilized to save code bits. Good average compression ratios up to about 13 can be achieved, as shown by experimental results.

A new color image compression technique based on moment-preserving and block truncation coding (BTC) is proposed. An input image is divided into non-overlapping blocks. And each block is coded by two representative colors, or equivalently, by two sets of tristimulus values (R/sub 1/, G/sub 1/, B/sub 1/) and (R/sub 2/, G/sub 2/, B/sub 2/). To compute the two representative colors of each block, some color moments of the block are preserved, and the two sets of desired tristimulus values are computed accordingly using analytic formulas. The compression ratio is 6 when the block size is 4/spl times/4. Furthermore, a simple method is applied to quantize the bit map into 64 predefined bit maps and requantize each color component from 8 bits into 4 bits. An average compression ratio of 14.00 can be achieved.>