Evaluation of Segmentation and Bias Field Correction in MR Brain Images Using Level Set and Multiplicative Intrinsic Component Optimization Methods

Segmentation of brain MR images is a major issue in medical image processing computations. In these images, segmentation is failed by the existence of internal artifact which is called intensity inhomogeneity due to the existence of overlap effect among brain tissue intensities which often causes false classification of brain tissues. In this paper, two suggested methods for segmentation and bias field correction arises, which these images are implemented through the level set (LSM) and multiplicative intrinsic component (MICO) algorithms. Methods outlined in this article include: bias field correction of the human brain MR images by one of these algorithms and segmentation by other algorithm and vice versa. Quantitative and qualitative analysis on the final results showed, accuracy above 90% for the area containing the CSF using the MICO algorithm as well as the areas WM and GM by LSM algorithm. These results can be used to select efficient algorithm to correct the bias field and segmenting each area, separately.