通過(guò)對(duì)法醫(yī)腎切片細(xì)胞的分割，統(tǒng)計(jì)計(jì)算腎臟細(xì)胞的面積，分析受害者腎臟細(xì)胞在顯
微鏡圖像下面積隨死亡時(shí)間變換的規(guī)律性，從而推測(cè)受害者遇害時(shí)間；基于直方圖的閾值分割是一種有效的分割技術(shù)，其關(guān)鍵是確定閾值。分別采用了迭代閾值法、最小模糊集閾值、最大熵閾值、最小偏差閾值比較研究腎臟細(xì)胞的分割結(jié)果。實(shí)驗(yàn)研究表明，迭代閾值法相對(duì)于其他閾值分割算法處理法醫(yī)腎細(xì)胞切片的分割效果較好，其原理簡(jiǎn)單，對(duì)圖像的噪聲具有一定的魯棒性。
關(guān)鍵詞：圖像分割 迭代閾值法 法醫(yī)圖像 JAVA
Research of Legal Medical Image Segmentation with Threshold Algorithms
He Xiang-Qian (School of Basic Medical Sciences, Chong Qing Medical University, Chongqing, 400016) Abstract: The objective of this paper is to segment the kidney cell in legal medical image to get the area of each cell and then analyze the relationship between the average area of cells and the death time of casualty in microscope image. The Histogram-based threshold segmentation is an effective technology. The key parameter in the threshold segmentation is the choice of the threshold value. This paper develops the iterative threshold, the minimum fuzziness threshold, the maximum entropy, the minimum error threshold methods to compare the result of segmentation. The experiment shows the result of portioning the kidney cell legal medical image with the iterative threshold method is better than other threshold algorithms. The iterative threshold segmentation is relative simple and robust against image noise.
Key Words: Image Segmentation, Iterative Threshold Method, Legal Image, Java