scholarly journals Tissue-Based Affine Registration of Brain Images to form a Vascular Density Atlas

2003 ◽  
pp. 9-15 ◽  
Author(s):  
Derek Cool ◽  
Dini Chillet ◽  
Jisung Kim ◽  
Jean-Phillipe Guyon ◽  
Mark Foskey ◽  
...  
Keyword(s):  
Vascular Density ◽  
Brain Images ◽  
Affine Registration

Improved Gauss-Newton optimisation methods in affine registration of SPECT brain images

2008 ◽  
Vol 44 (22) ◽  
pp. 1291 ◽  
Author(s):  
D. Salas-Gonzalez ◽  
J.M. Górriz ◽  
J. Ramírez ◽  
A. Lass ◽  
C.G. Puntonet
Keyword(s):  
Brain Images ◽  
Affine Registration ◽  
Optimisation Methods

scholarly journals Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
D. Salas-Gonzalez ◽  
J. M. Górriz ◽  
J. Ramírez ◽  
P. Padilla ◽  
I. A. Illán
Keyword(s):  
Convergence Rate ◽  
Optimization Algorithm ◽  
Histogram Equalization ◽  
Brain Images ◽  
Affine Registration ◽  
Registration Algorithm ◽  
Affine Model ◽  
The Cost ◽  
General Affine ◽  
Brain Template

A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.

A global optimisation method for robust affine registration of brain images

2001 ◽  
Vol 5 (2) ◽  
pp. 143-156 ◽  
Author(s):  
Mark Jenkinson ◽  
Stephen Smith
Keyword(s):  
Global Optimisation ◽  
Brain Images ◽  
Affine Registration

Affine Registration Based on Chord Height Point and Genetic Algorithm

2014 ◽  
Vol 39 (9) ◽  
pp. 1447-1457
Author(s):  
Gui-Mei ZHANG ◽  
Shao-Bo JIANG ◽  
Jun CHU
Keyword(s):  
Genetic Algorithm ◽  
Affine Registration

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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