A fully automated approach is presented to extract brain areas efficiently from FDG-PET head scans. A threshold value is automatically calculated from the histogram graph of the brain images, followed by region growing and morphological operations, to segment brain areas from these images. Next, the midsagittal lines on axial slices are detected to separate the brain into two hemispheres. The proposed approach has been applied to 226 cases of normal controls and patients with neurological diseases. The average processing time is about 3 seconds on a standard personal computer. The experiment has shown promising results
Abstract. For simulation of surgical operations, extraction of selected regions from MR images requi...
Automated segmentation of brain lesions in magnetic resonance images (MRI) is a difficult procedure ...
Abstract Automatic brain abnormality detection is a major challenge in medical image processing. Man...
We propose and evaluate an automatic segmentation method for extracting striatal brain structures (c...
An automated method for segmenting magnetic resonance head images into brain and non-brain has been ...
A fully automated approach is presented to extract brain efficiently from computed tomography (CT) h...
We propose and evaluate an automatic segmentationmethod for extracting striatal brain structures (ca...
The study of structural and functional magnetic resonance imaging data has greatly benefitted from t...
The paper presents an algorithm of segmentation of brain imaging examination results − computed tomo...
The cortical surface of the human brain consists of a large number of folds forming valleys and ridg...
In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction te...
Objectives: Introduction of a new atlas-based method for analyzing functional data which takes into ...
Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy tr...
Brain portion extraction from magnetic resonance image (MRI) of human head scan is an important proc...
This paper introduces an interactive and intelligent approach for accurate brain segmentation. A hig...
Abstract. For simulation of surgical operations, extraction of selected regions from MR images requi...
Automated segmentation of brain lesions in magnetic resonance images (MRI) is a difficult procedure ...
Abstract Automatic brain abnormality detection is a major challenge in medical image processing. Man...
We propose and evaluate an automatic segmentation method for extracting striatal brain structures (c...
An automated method for segmenting magnetic resonance head images into brain and non-brain has been ...
A fully automated approach is presented to extract brain efficiently from computed tomography (CT) h...
We propose and evaluate an automatic segmentationmethod for extracting striatal brain structures (ca...
The study of structural and functional magnetic resonance imaging data has greatly benefitted from t...
The paper presents an algorithm of segmentation of brain imaging examination results − computed tomo...
The cortical surface of the human brain consists of a large number of folds forming valleys and ridg...
In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction te...
Objectives: Introduction of a new atlas-based method for analyzing functional data which takes into ...
Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy tr...
Brain portion extraction from magnetic resonance image (MRI) of human head scan is an important proc...
This paper introduces an interactive and intelligent approach for accurate brain segmentation. A hig...
Abstract. For simulation of surgical operations, extraction of selected regions from MR images requi...
Automated segmentation of brain lesions in magnetic resonance images (MRI) is a difficult procedure ...
Abstract Automatic brain abnormality detection is a major challenge in medical image processing. Man...