基于改进的大津方法与区域生长的医学图像分割

提出了一种自动分割边缘模糊图像的新方法.该方法利用小波变换对图像进行压缩与多解像度分解,通过强调各解像度上高周波成分的极值进行图像锐化;利用邻域平均与中值滤波方法对图像进行平滑;利用改进的大津方法与多种子投票机制的区域生长方法进行图像的自动分割;根据分割效果对阈值进行迭代控制;通过自动分割的区域与影像医生手动分割区域的误差、平均最短距离进行自动分割效果的评价.将本方法应用于实际的30幅边缘模糊的腹部MRI图像,证明了其对复杂图像分割的有效性.

Medical Image Segmentation Based on Improved Ostu Algorithm and Regional Growth Algorithm

A new method is proposed for automatic segmentation of edge-blurred medical images.by the which the images are compressed and dissected in multi-resolution through wavelet transform.The images are sharpened by strengthening the extremum of the high-frequents elements at each and every resolution and smoothened by neighboring average and median filtering.Then,the images are segmented automatically using the improved Ostu algorithm(maximization of interclass variance) and the regional growth algorithm through multiseed vote mechanism and,according to the segmenting effect,the threshold is controlled iteratively to evaluate the mean minimum distance with the error found in between the region segmented automatically and that segmented manually.The method proposed has been applied to 30 edge-blurred abdominal MRI images and proves its effectiveness for complex image segmentation.