Fast free-form deformation using graphics processing units
Computer Methods and Programs in Biomedicine
Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
A review of atlas-based segmentation for magnetic resonance brain images
Computer Methods and Programs in Biomedicine
Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches
Information Sciences: an International Journal
Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI
Computers in Biology and Medicine
Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Supervised range-constrained thresholding
IEEE Transactions on Image Processing
Automatic segmentation of human facial tissue by MRI-CT fusion: A feasibility study
Computer Methods and Programs in Biomedicine
Automatic classification of eye activity for cognitive load measurement with emotion interference
Computer Methods and Programs in Biomedicine
Muscle computer interfaces for driver distraction reduction
Computer Methods and Programs in Biomedicine
Breast mass contour segmentation algorithm in digital mammograms
Computer Methods and Programs in Biomedicine
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Brain extraction, also known as skull stripping, is one of the most important preprocessing steps for many automatic brain image analysis. In this paper we present a new approach called Multispectral Adaptive Region Growing Algorithm (MARGA) to perform the skull stripping process. MARGA is based on a region growing (RG) algorithm which uses the complementary information provided by conventional magnetic resonance images (MRI) such as T1-weighted and T2-weighted to perform the brain segmentation. MARGA can be seen as an extension of the skull stripping method proposed by Park and Lee (2009) [1], enabling their use in both axial views and low quality images. Following the same idea, we first obtain seed regions that are then spread using a 2D RG algorithm which behaves differently in specific zones of the brain. This adaptation allows to deal with the fact that middle MRI slices have better image contrast between the brain and non-brain regions than superior and inferior brain slices where the contrast is smaller. MARGA is validated using three different databases: 10 simulated brains from the BrainWeb database; 2 data sets from the National Alliance for Medical Image Computing (NAMIC) database, the first one consisting in 10 normal brains and 10 brains of schizophrenic patients acquired with a 3T GE scanner, and the second one consisting in 5 brains from lupus patients acquired with a 3T Siemens scanner; and 10 brains of multiple sclerosis patients acquired with a 1.5T scanner. We have qualitatively and quantitatively compared MARGA with the well-known Brain Extraction Tool (BET), Brain Surface Extractor (BSE) and Statistical Parametric Mapping (SPM) approaches. The obtained results demonstrate the validity of MARGA, outperforming the results of those standard techniques.