A New Approach to 3D Sulcal Ribbon Finding from MR Images

Several segmentation methods are implemented and applied to segment the facial masseter tissue from magnetic resonance images. The common idea for all methods is to take advantage of prior information from different MR images belonging to different individuals in segmentation of a test MR image. Standard atlas-based segmentation methods and probabilistic segmentation methods based on Markov random field use labeled prior information. In this study, a new approach is also proposed where unlabeled prior information from a set of MR images is used to segment masseter tissue in a probabilistic framework. The proposed method uses only a seed point that indicates the target tissue and performs automatic segmentation for the selected tissue without using labeled training set. The segmentation results of all methods are validated and compared where the influences of labeled or unlabeled prior information and initialization are discussed particularly. It is shown that if appropriate modeling is done, there is no need for labeled prior information. The best accuracy is obtained by the proposed approach where unlabeled prior information is used.

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Magnetic Resonance Elastography of Nucleus Pulposus Shear Modulus: A New Approach for Disc Degeneration Biomarkers

ASME 2012 Summer Bioengineering Conference, Parts A and B ◽

10.1115/sbc2012-80537 ◽

2012 ◽

Author(s):

Daniel H. Cortes ◽

Jeremy F. Magland ◽

Alexander C. Wright ◽

Victor H. Barocas ◽

Dawn M. Elliott

Keyword(s):

Magnetic Resonance ◽

Early Diagnosis ◽

Shear Modulus ◽

Disc Degeneration ◽

Intervertebral Disc Degeneration ◽

Structural Changes ◽

Morphological Changes ◽

Mr Images ◽

New Approach ◽

Advanced Stages

Intervertebral disc degeneration is cell mediated cascade of biochemical, mechanical and structural changes that disrupts its function. These changes are difficult to overturn; consequently, early diagnosis is key for the success of any treatment. Clinically, disc degeneration is diagnosed by the interpretation of morphological changes observed in T2 weighted MR images. However, those changes in morphology are characteristic of moderate to advanced stages of degeneration. Therefore, this method is not useful to diagnose early stages of disc degeneration.

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A Robust Method for MR Image Segmentation and Multiple Scleroses Detection

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2019.2690 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1119-1130

Author(s):

H. Zouaoui ◽

A. Moussaoui ◽

M. Oussalah ◽

A. Taleb-Ahmed

Keyword(s):

White Matter ◽

Automatic Segmentation ◽

Fuzzy Model ◽

Mr Images ◽

New Approach ◽

Magnetic Resonance Imaging Mri ◽

Mr Image Segmentation ◽

The Brain

In the present article, we propose a new approach for the segmentation of the MR images of the Multiple Sclerosis (MS). The Magnetic Resonance Imaging (MRI) allows the visualization of the brain and it is widely used in the diagnosis and the follow-up of the patients suffering from MS. Aiming to automate a long and tedious process for the clinician, we propose the automatic segmentation of the MS lesions. Our algorithm of segmentation is composed of three stages: segmentation of the brain into regions using the algorithm Fuzzy Particle Swarm Optimization (FPSO) in order to obtain the characterization of the different healthy tissues (White matter, grey matter and cerebrospinal fluid (CSF)) after the extraction of white matter (WM), the elimination of the atypical data (outliers) of the white matter by the algorithm Fuzzy C-Means (FCM), finally, the use of a Mamdani-type fuzzy model to extract the MS lesions among all the absurd data.

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Seeded Region Growing Based on Extension for Multispectral MR Images Classification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1079-1080.872 ◽

2014 ◽

Vol 1079-1080 ◽

pp. 872-877

Author(s):

Yen Che Chang ◽

Kuei Ting Kuo ◽

Zih Yi Wang ◽

Chuin Mu Wang

Keyword(s):

Cerebral Spinal Fluid ◽

Brain Mri ◽

Medical Knowledge ◽

Region Growing ◽

Mr Images ◽

Brain Images ◽

New Approach ◽

The Past ◽

Seeded Region Growing ◽

Effective Diagnosis

In the past, doctors judged images based on their own medical knowledge. Nowadays, the digital image processing technology can alleviate the burden of judging a large amount of multispectral information and lead to more effective diagnosis of the pathological tissues. In this paper, we propose a new approach of seeded region growing based on extension (SRGBE) to classify tissues from brain MRI. Based on extension, we tried to strengthen the regional definition. First, we use seeded region growing (SRG) to segment brain images. Second, the SRGBE result is further classified by K-means. Finally, we compare the images of gray matter, white matter and cerebral spinal fluid produced by both approaches to demonstrate the performance of SRGBE.

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Vascular Maps on Contrast-enhanced Breast MR Images: A New Approach Requiring Further Investigation

Radiology ◽

10.1148/radiol.2471071737 ◽

2008 ◽

Vol 247 (1) ◽

pp. 295-296 ◽

Cited By ~ 7

Author(s):

Francesco Sardanelli

Keyword(s):

Mr Images ◽

New Approach ◽

Contrast Enhanced ◽

Breast Mr

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A new approach to study cardiac motion: The optical flow of cine MR images

Magnetic Resonance in Medicine ◽

10.1002/mrm.1910290112 ◽

1993 ◽

Vol 29 (1) ◽

pp. 59-67 ◽

Cited By ~ 35

Author(s):

Sundar C. Amartu ◽

Hubert J. Vesselle

Keyword(s):

Optical Flow ◽

Cardiac Motion ◽

Mr Images ◽

New Approach ◽

Cine Mr

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Segmentation of MR Images using Curve Evolution and Prior Information

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001497000573 ◽

1997 ◽

Vol 11 (08) ◽

pp. 1233-1245 ◽

Cited By ~ 9

Author(s):

Homer H. Pien ◽

Mukund Desai ◽

Jayant Shah

Keyword(s):

Strength Function ◽

Region Of Interest ◽

Curve Evolution ◽

Manual Segmentation ◽

Segmentation Method ◽

Mr Images ◽

New Approach ◽

Human Interactions ◽

Edge Strength ◽

Size Accuracy

Segmentation of anatomic structures of the human brain from MR images is important for assessing treatment efficacy, screening for anomalies, and improving our understanding of human development. The labor intensive nature of manual segmentation, however, makes such a technique viable only in selected cases. In this paper we present a new approach to segmentation that involves only minimal human interactions. The technique utilizes a variational formulation to obtain an edge-strength function over the region of interest, and uses curve evolution and a pre-segmented atlas to guide the actual segmentation process. The approach is demonstrated via both phantoms and actual MR images, and when applied to the lateral ventricles and caudate nucleus, showed a size accuracy error of 5%–20% with respect to manual segmentation, depending on the manual segmentation method utilized.

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