TU-F-BRF-06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering

2014 ◽  
Vol 41 (6Part27) ◽  
pp. 472-472
Author(s):  
S Gou ◽  
S Rapacchi ◽  
P Hu ◽  
K Sheng
Keyword(s):  
Dictionary Learning ◽  
Mri Segmentation ◽  
Manifold Clustering

scholarly journals Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation

2015 ◽  
Vol 19 (5) ◽  
pp. 1598-1609 ◽  
Author(s):  
Snehashis Roy ◽  
Qing He ◽  
Elizabeth Sweeney ◽  
Aaron Carass ◽  
Daniel S. Reich ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Brain Mri ◽  
Mri Segmentation ◽  
Sparse Dictionary Learning ◽  
Subject Specific

Accurate MRI Segmentation of Skull Base Meningiomas

2016 ◽  
Vol 77 (S 02) ◽  
Author(s):  
Francesco Latini ◽  
Elna-Marie Larsson ◽  
Mats Ryttlefors
Keyword(s):  
Skull Base ◽  
Mri Segmentation ◽  
Skull Base Meningiomas

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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