基于粗糙集和属性直方图的医学图像增强

医学图像信息存在着复杂性,在处理中的各个不同层次可能出现不完整性和不确定性。利用粗糙集理论进行图像增强,子图的划分是关键。属性直方图是对直方图概念的推广,是一种由先验知识约束的直方图,将它用于子图划分。在此基础上本文提出了一种基于粗糙集和属性直方图的医学图像增强方法。利用属性直方图的Otsu算法确定灰度阈值,根据灰度阈值利用不可分辨关系,将图像划分为背景子图、目标子图和噪声子图,对噪声点用中值滤波进行滤除,去噪后的背景子图和目标子图进行增强变换,合并得到增强图像。以胸部CT图像中的肺组织为目标区域,进行大量的实验,结果表明该方法明显增强了图像且不损害图像的边缘。

Medical image enhancement using rough sets and bound histogram

A medical image edge detect method based on granular computing.Integrality and uncertainty may occur in many different levels in the process of medical image because of the complication of image information.It is a crucial problem to partition an image into different sub-images when rough sets theory is applied in image enhancement.The improved histogram,a histogram bound by some prior knowledge,is used for partitioning an image into different sub-images,Furthermore,all image enhancement method based on rough sets and bound histogram is proposed.There are three steps in the method.First,the gray-level threshold is determined by Otsu algorithm based on bound histogram.Second,based on the indiscernible relation,according to the threshold,an image is partitioned into sub-images for back-ground,object and noise.Noise pixels can be removed by median filtering.Third,the denoised sub-images of background and object are enhanced respectively and they are combined to form a final enhanced image.Regard lung tissue in the chest CT picture as the target area,experimental results show that the image is remarkably enhanced and the boundaries of a region of interest keep unchanged in shape.