Purpose—To develop and implement an automated and robust technique to extract brain from T2-weighted images. Materials and Methods—Magnetic resonance imaging (MRI) was performed on 75 adult volunteers to acquire dual fast spin echo (FSE) images with fat-saturation technique on a 3T Philips scanner. Histogram-derived thresholds were derived directly from the original images followed by the application of regional labeling, regional connectivity, and mathematical morphological operations to extract brain from axial late-echo FSE (T2-weighted) images. The proposed technique was evaluated subjectively by an expert and quantitatively using Bland-Altman plot and Jaccard and Dice similarity measures. Results—Excellent agreement between the extract...
International audienceTo isolate the brain from non-brain tissues using a fully automatic method may...
In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is pres...
The paper presents a new approach to segmentation of brain from the MR studies. The method is fully ...
An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D)...
An automated method for segmenting magnetic resonance head images into brain and non-brain has been ...
This work discusses fully automated extraction of brain tumor and edema in 3D MR volumes. The goal o...
Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) s...
This paper introduces an interactive and intelligent approach for accurate brain segmentation. A hig...
Extraction of the brain—i.e. cerebrum, cerebellum, and brain stem—from T1-weighted structural magnet...
Accurate and fully automatic segmentation of brain from magnetic resonance (MR)scans is a challengin...
AbstractThe aim of this work is to propose the fully automated pathological area extraction from mul...
Abstract Background The extraction of brain tissue from magnetic resonance head images, is an import...
Abstract. For simulation of surgical operations, extraction of selected regions from MR images requi...
The study of structural and functional magnetic resonance imaging data has greatly benefitted from t...
The work presented in this dissertation involves the development of parametric magnetic resonance im...
International audienceTo isolate the brain from non-brain tissues using a fully automatic method may...
In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is pres...
The paper presents a new approach to segmentation of brain from the MR studies. The method is fully ...
An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D)...
An automated method for segmenting magnetic resonance head images into brain and non-brain has been ...
This work discusses fully automated extraction of brain tumor and edema in 3D MR volumes. The goal o...
Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) s...
This paper introduces an interactive and intelligent approach for accurate brain segmentation. A hig...
Extraction of the brain—i.e. cerebrum, cerebellum, and brain stem—from T1-weighted structural magnet...
Accurate and fully automatic segmentation of brain from magnetic resonance (MR)scans is a challengin...
AbstractThe aim of this work is to propose the fully automated pathological area extraction from mul...
Abstract Background The extraction of brain tissue from magnetic resonance head images, is an import...
Abstract. For simulation of surgical operations, extraction of selected regions from MR images requi...
The study of structural and functional magnetic resonance imaging data has greatly benefitted from t...
The work presented in this dissertation involves the development of parametric magnetic resonance im...
International audienceTo isolate the brain from non-brain tissues using a fully automatic method may...
In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is pres...
The paper presents a new approach to segmentation of brain from the MR studies. The method is fully ...