Brain MR image segmentation based on an improved active contour model

Abstract

It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%.

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

“…They use internal and external forces to delimit the limits of objects and thus distort images. We could distinguish parametric models (active contours or snake) [101][102][103][104][105] and geometric models [106]. They are robust to noise and parasitic edges thank to their ability to generate closed parametric surfaces or curves.…”

Development of technique for face detection in image based on binarization, scaling and segmentation methods

“…-regions boundaries determination (pixels with a large intensity gradient, as well as pixels differing in color, are selected as the regions boundaries) [8];…”

A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

“…At present, there are many segmentation methods for brain MR image, such as threshold method [2], region method [3], random field method [4], neural network method [5] and clustering method [6][7][8][9]. As one of the main techniques of unsupervised machine learning, clustering method, especially fuzzy c-means (FCM) clustering method [10], has been widely used in brain MR image segmentation.…”