Hidden Markov models-based 3D MRI brain segmentation

Ibrahim, Mohamed O.; John, Nigel; Kabuka, Mansur R.; Younis, Akmal A.

doi:10.1016/j.imavis.2006.03.001

Cited by 48 publications

(56 citation statements)

References 30 publications

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“…Since these methods use MRI signal contrast to detect abnormalities, they can be modifi ed or extended to evaluate lesion characteristics seen with ischemia or stroke [ 23,24 ] . Reviewed below are some of the different available approaches, focused on automated methods such as GMMs [ 25 ] , MRFs [ 26 ] , normalized graph cut [ 27 ] , and K-means clustering [ 28 ] for the detection of brain abnormalities. Although many of these methods have considerable similarities and are sometimes indistinguishable, we describe them separately.…”

Section: Current Computational Approaches For Lesion Detectionmentioning

confidence: 99%

“…The signifi cance of their work is that they demonstrated that stroke territories shift over time. Hidden Markov models (HMM), a variant of multidimensional MRF, have been used to fi nd WM-GM-CSF tissue types [ 26 ] . In another recent study, 3D Hilbert-Peano mapping was used to convert 3D T1 and FLAIR data into 1D data for computational ease [ 19 ] .…”

Section: Markov Random Fieldsmentioning

confidence: 99%

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Computational Analysis: A Bridge to Translational Stroke Treatment

Ghosh

Sun

Turenius

et al. 2012

Translational Stroke Research

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Objective rapid quantifi cation of injury using computational methods can improve the assessment of the degree of stroke injury, aid in the selection of patients for early or specifi c treatments, and monitor the evolution of injury and recovery. In this chapter, we use neonatal ischemia as a case-study of the application of several computational methods that in fact are generic and applicable across the age and disease spectrum. We provide a summary of current computational approaches used for injury detection, including Gaussian mixture models (GMM), Markov random fi elds (MRFs), normalized graph cut, and K-means clustering. We also describe more recent automated approaches to segment the region(s) of ischemic injury including hierarchical region splitting, support vector machine, a brain symmetry/asymmetry integrated model, and a watershed method that are robust at different developmental stages. We conclude with our assessment of probable future research directions in the fi eld of computational noninvasive stroke analysis such as automated detection of the ischemic core and penumbra, monitoring of implanted neuronal stem cells in the ischemic brain, injury localization specifi c to different brain anatomical regions, and quantifi cation of stroke evolution, recovery and spatiotemporal interactions between injury volume/severity and treatment. Computational analysis is expected to open a new horizon in current clinical and translational stroke research by exploratory data mining that is not detectable using the standard "methods" of visual assessment of imaging data.

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Section: Current Computational Approaches For Lesion Detectionmentioning

confidence: 99%

Section: Markov Random Fieldsmentioning

confidence: 99%

Computational Analysis: A Bridge to Translational Stroke Treatment

Ghosh

Sun

Turenius

et al. 2012

Translational Stroke Research

View full text Add to dashboard Cite

show abstract

“…Secondly in region-based segmentation many techniques have been developed like region growing technique [8], split-and-merge based technique [9], and watershed based technique [10]. And Finally Classification-based segmentation includes statistical based classification such as in [11] Markov random field is used, in [12] Hidden Markov models based technique is proposed, and in [13] Gaussian mixture model segmentation is proposed. The other sub categories of classification based segmentation are supervised learning based segmentation and unsupervised learning based segmentation.…”

Section: Related Workmentioning

confidence: 99%

Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

Jaffar¹,

Zia²,

Latif³

et al. 2012

IJCIS

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In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and analyze the brain tumor. We proposed a multi-phase segmentation and visualization technique which overcomes the many problems of 3D volume segmentation methods like lake of fine details. In this system segmentation is done in three different phases which reduces the error chances. The system finds contours for skull, brain and tumor. These contours are stacked over and two novel methods are used to find the 3D visualization models. The results of these techniques, particularly of interpolation based, are impressive. Proposed system is tested against publically available data set [41] and MRI datasets available from MRI & CT center Rawalpindi, Pakistan [42].

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“…Results for image segmentation means of computer image processing, such as Gaussian filtering, it also included some De-noising practices. For fMRI timing image set, timing approach would be applied, such as hidden Markov chain model [19]. In addition, the timing set based feature-extraction after pre-processing of fMRI, fuzzy clustering methods, and rule-based fuzzy inference system played an important role in the calculation of fMRI.…”

Section: Introductionmentioning

confidence: 99%