A New Approach to Local Contrast Enhancement of Medical Image Based on Multiscale Morphology
WU Wenfan · Beijing shengwu yixue gongcheng · 2005
The paper presents a more efficient and accurate method for medical image enhancement. In research we find some details can be removed by enhancing dark features and noise be amplified by emphasizing smaller features. So the features are extracted from recursively opened images by white tophat transformation. To avoid some gray-level bias we propose the method of normalized gray-level under condition. In this algorithm, the morphological operations are non-dual and the contrast stretching operations are addition instead of multiplication. The algorithm is tested by MR images and compared with the existing method based on multiscale morphology. The experimental results show that the method is more effective, less sensitive to noise and preserving processed images more accurately.Moreover, the algorithm works with one-fourth features as many as previous morphological method for local contrast enhancement and reduces computational cost.