Automatic Brain Extraction for T1-Weighted Magnetic Resonance Images Using Region Growing

2016 ◽  
Author(s):  
Yu-Lung Ho ◽  
Wei-Yang Lin ◽  
Chia-Ling Tsai ◽  
Cheng-Chia Lee ◽  
Chih-Yang Lin
Keyword(s):  
Magnetic Resonance ◽  
Region Growing ◽  
Magnetic Resonance Images ◽  
Brain Extraction

An Improvement Method of Brain Extraction Tools for Magnetic Resonance Images

2014 ◽  
Vol 4 (6) ◽  
pp. 895-900 ◽  
Author(s):  
Weitun Yang ◽  
Minli Liao ◽  
Xiaojie Zhang ◽  
Weibei Dou ◽  
Mingyu Zhang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Brain Extraction ◽  
Improvement Method

scholarly journals A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head

2016 ◽  
Vol 257 ◽  
pp. 185-193 ◽  
Author(s):  
Adam Delora ◽  
Aaron Gonzales ◽  
Christopher S. Medina ◽  
Adam Mitchell ◽  
Abdul Faheem Mohed ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Rapid Process ◽  
Brain Extraction

scholarly journals Atlas-Free Cervical Spinal Cord Segmentation on Midsagittal T2-Weighted Magnetic Resonance Images

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Chun-Chih Liao ◽  
Hsien-Wei Ting ◽  
Furen Xiao
Keyword(s):  
Spinal Cord ◽  
Magnetic Resonance ◽  
Expectation Maximization ◽  
Cervical Spinal Cord ◽  
Region Growing ◽  
Magnetic Resonance Images ◽  
Spinal Diseases ◽  
Intervertebral Disks ◽  
Vertebral Bodies ◽  
Aided Diagnosis

An automatic atlas-free method for segmenting the cervical spinal cord on midsagittal T2-weighted magnetic resonance images (MRI) is presented. Pertinent anatomical knowledge is transformed into constraints employed at different stages of the algorithm. After picking up the midsagittal image, the spinal cord is detected using expectation maximization and dynamic programming (DP). Using DP, the anterior and posterior edges of the spinal canal and the vertebral column are detected. The vertebral bodies and the intervertebral disks are then segmented using region growing. Then, the anterior and posterior edges of the spinal cord are detected using median filtering followed by DP. We applied this method to 79 noncontrast MRI studies over a 3-month period. The spinal cords were detected in all cases, and the vertebral bodies were successfully labeled in 67 (85%) of them. Our algorithm had very good performance. Compared to manual segmentation results, the Jaccard indices ranged from 0.937 to 1, with a mean of 0.980 ± 0.014. The Hausdorff distances between the automatically detected and manually delineated anterior and posterior spinal cord edges were both 1.0 ± 0.5 mm. Used alone or in combination, our method lays a foundation for computer-aided diagnosis of spinal diseases, particularly cervical spondylotic myelopathy.

Automatic Diagnosis of Brain Magnetic Resonance Images based on Riemannian Geometry

2015 ◽  
Vol 13 (2) ◽  
pp. 30-40
Author(s):  
Mohamed Gouskir ◽  
Belaid Bouikhalene ◽  
Hicham Aissaoui ◽  
Benachir Elhadadi
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Riemannian Manifolds ◽  
Tumor Detection ◽  
Brain Regions ◽  
Magnetic Resonance Images ◽  
Automatic Diagnosis ◽  
Brain Extraction ◽  
Brain Magnetic Resonance Image ◽  
Brain Image Analysis

Automated brain tumor detection and segmentation, from medical images, is one of the most challenging. The authors present, in this paper, an automatic diagnosis of brain magnetic resonance image. The goal is to prepare the image of the human brain to locate the existence of abnormal tissues in multi-modal brain magnetic resonance images. The authors start from the image acquisition, reduce information, brain extraction, and then brain region diagnosis. Brain extraction is the most important preprocessing step for automatic brain image analysis. The authors consider the image as residing in a Riemannian space and they based on Riemannian manifold to develop an algorithm to extract brain regions, these regions used in other algorithm to brain tumor detection, segmentation and classification. Riemannian Manifolds show the efficient results to brain extraction and brain analysis for multi-modal resonance magnetic images.

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