Brightness preserving optimized weighted bi-histogram equalization algorithm and its application to MR brain image segmentation

Medical image segmentation is crucial for neuroscience research and computer-aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast-enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub-histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub-histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.