Fully automatic extraction of human spine curve from MR images using methods of efficient intervertebral disk extraction and vertebra registration

2010 ◽  
Vol 6 (1) ◽  
pp. 21-33 ◽  
Author(s):  
Zhenyu Tang ◽  
Josef Pauli
Keyword(s):  
Intervertebral Disk ◽  
Automatic Extraction ◽  
Mr Images ◽  
Human Spine ◽  
Fully Automatic

Fully automatic prostate segmentation in MR images using a new hybrid active contour-based approach

2018 ◽  
Vol 12 (8) ◽  
pp. 1629-1637 ◽  
Author(s):  
Ahad Salimi ◽  
Mohammad Ali Pourmina ◽  
Mohammad-Shahram Moin
Keyword(s):  
Active Contour ◽  
Mr Images ◽  
Prostate Segmentation ◽  
Fully Automatic

Fully automatic, retrospective enhancement of real-time acquired cardiac cine MR images using image-based navigators and respiratory motion-corrected averaging

2008 ◽  
Vol 59 (4) ◽  
pp. 771-778 ◽  
Author(s):  
Peter Kellman ◽  
Christophe Chefd'hotel ◽  
Christine H. Lorenz ◽  
Christine Mancini ◽  
Andrew E. Arai ◽  
...  
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Mr Images ◽  
Cine Mr ◽  
Cardiac Cine ◽  
Fully Automatic

scholarly journals Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)

2013 ◽  
Author(s):  
Qaiser Mahmood ◽  
Mohammad Alipoor ◽  
Artur Chodorowski ◽  
Andrew Mehnert1 ◽  
Mikael Persson
Keyword(s):  
Mean Shift ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Brain Segmentation ◽  
Mr Images ◽  
Unsupervised Segmentation ◽  
Fully Automatic ◽  
The Brain ◽  
Mr Brain ◽  
Dice Index

In this paper, we validate our proposed segmentation algorithm called Bayesian-based adaptive mean-shift (BAMS) on real mul-timodal MR images provided by the MRBrainS challenge. BAMS is a fully automatic unsupervised segmentation algorithm. It is based on the adaptive mean shift wherein the adaptive bandwidth of the kernel for each feature point is estimated using our proposed Bayesian approach [1]. BAMS is designed to segment the brain into three tissues; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The performance of the algorithm is evaluated relative to the manual segmentation (ground truth). The results of our proposed algorithm show the average Dice index 0.8377±0.036 for the WM, 0.7637±0.038 for the GM and 0.6835 ±0.023 for the CSF.

ENHANCEMENT TECHNIQUES FOR MRI HUMAN SPINE IMAGES

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Aqilah Baseri Huddin ◽  
W Mimi Diyana W Zaki ◽  
Agnes Chung Wai Mun ◽  
Ling Chei Siong ◽  
Hamzaini Abdul Hamid
Keyword(s):  
Magnetic Resonance Image ◽  
Discrete Wavelet ◽  
Mr Images ◽  
Image Preprocessing ◽  
Image Processing Techniques ◽  
Human Spine ◽  
Human Soft Tissue ◽  
Herniated Disk ◽  
Processing Techniques

The quality of Magnetic Resonance Image (MRI) determines the accuracy of clinical diagnosis. It provides information about the human soft tissue anatomy. MRI of spine is used by the physicians to evaluate any presence of diseases including slipped disk, herniated disk, trauma and disk degeneration. Existence of noises and artifacts can degrade the quality of the MR images. Thus, appropriate image processing techniques may help to improve the quality of the acquired image. Preprocessing is usually done to remove the noise, enhance an image boundary and adjust the image contrast. Current techniques to enhance and reduce noise in MRI human spine are discussed and a method using discrete wavelet transform to enhance the MRI of human spine is proposed. The resultant images are evaluated quantitatively. This study shows that the proposed method has better results as compared to other existing method based on evaluation tests. 

scholarly journals A fully automatic unsupervised segmentation framework for the brain tissues in MR images

2014 ◽  
Author(s):  
Qaiser Mahmood ◽  
Artur Chodorowski ◽  
Babak Ehteshami Bejnordi ◽  
Mikael Persson
Keyword(s):  
Mr Images ◽  
Unsupervised Segmentation ◽  
Fully Automatic ◽  
Segmentation Framework ◽  
The Brain ◽  
Brain Tissues
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