Urinary Sediment Images Segmentation Based on Efficient Gabor Filters
(0) Urinary sediments are very important to help diagnose diseases such as kidney inflammation, urethra inflammation, bladder inflammation and so on. Gabor filter is a widely used feature extraction method, especially in image texture analysis. The selection of optimal filter parameters is usually problematic and unclear. This paper present a improved and robust method, algorithm based on efficient Gabor filters in combination with simulated annealing and K-means clustering, for urinary sediment segmentation. And the method presented are consist of two steps: first, using multi-channel Gabor-filters to extract the features of the urinary sediment from the microimages; second, segment the urinary sediment image by simulated annealing and K-means clustering. The experiment results show that the method can segment the urinary sediment images effectively and precisely.
Shi Zhang Jun-hui Wang Shan-guo Zhao Xin-jun Luan
Electronic Information Engineering Department, Northeastern University, Shenyang, China 110004
国际会议
北京
英文
816-820
2007-05-23（万方平台首次上网日期，不代表论文的发表时间）