Accurate and robust extraction of brain regions using a deformable model based on radial basis functions

Liu, Jia Xiu; Chen, Yongsheng; Chen, Li Fen

doi:10.1016/j.jneumeth.2009.05.011

Cited by 29 publications

(28 citation statements)

References 54 publications

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“…The 3D mathematical morphology is used for skull stripping other tissues. A method based on an implicit deformable model which is described by radial basis functions is introduced by Liu et al [70] for skull stripping.…”

Section: Deformable Surface-based Methodsmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

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Section: Deformable Surface-based Methodsmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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“…Some of the most common shape analysis applications of deformable models are in the areas of segmentation and volume analysis, along with shape matching and registration. [118] 1999 A 3D 100 C Smith [119] 2002 A 3D 45 C Zhuang et al [120] 2006 A 3D 49 C Joshi et al [121] 2007 A 3D 6 N Tu et al [122] 2007 A 3D 28 C Huang et al [123] 2009 A 3D 36 C Liu et al [124] 2009 A 3D 38 N Li et al [125] 2011 A 3D 5 N Hashioka et al [126] 2012 SA 3D 14 C or classification.…”

Section: Deformable Modelsmentioning

confidence: 99%

“…Liu et al [124] suggested a deformable model that was driven by radial basis functions to be used for automated extraction of the brain. This model proved to be an accurate and fast technique, having a similar accuracy to the BET proposed by Smith [119].…”

Section: Deformable Modelsmentioning

confidence: 99%

Shape analysis of the human brain.

Nitzken¹,

Joseph²

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DEDICATIONSometimes our light goes out but is blown into flame by another human being.Each of us owes deepest thanks to those who have rekindled this light. Autism is a complex developmental disability that has dramatically increased in prevalence, having a decisive impact on the health and behavior of children. Methods used to detect and recommend therapies have been much debated in the medical community because of the subjective nature of diagnosing autism. In order to provide an alternative method for understanding autism, the current work has developed a 3-dimensional state-of-the-art shape based analysis of the human brain to aid in creating more accurate diagnostic assessments and guided risk analyses for individuals with neurological conditions, such as autism. Methods:The aim of this work was to assess whether the shape of the human brain can be used as a reliable source of information for determining whether an individual will be diagnosed with autism. The study was conducted using multi-center databases of magnetic resonance images of the human brain. The subjects in the databases were analyzed using a series of algorithms consisting of bias correction, skull stripping, multi-label brain segmentation, 3-dimensional mesh construction, spherical harmonic decomposition, registration, and classification. The software algorithms were developed as an original contribution of this dissertation in collaboration with the BioImaging Laboratory at the University of Louisville Speed School of Engineering. The classification of each subject was used to construct diagnoses v and therapeutic risk assessments for each patient.Results: A reliable metric for making neurological diagnoses and constructing therapeutic risk assessment for individuals has been identified. The metric was explored in populations of individuals having autism spectrum disorders, dyslexia, Alzheimers disease, and lung cancer. Conclusion:Currently, the clinical applicability and benefits of the proposed software approach are being discussed by the broader community of doctors, therapists, and parents for use in improving current methods by which autism spectrum disorders are diagnosed and understood.vi

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“…In the literature, many brain extraction methods have been proposed. These methods use different techniques, namely, deformable models [155][156][157], atlas-based [158,159], and label fusion and hybrid algorithms [160][161][162]. However, most of the existing techniques were designed to work primarily for T1-weighted MR brain images, thus, they are not well suited to extract the brain from diffusion-weighted images directly.…”

Section: Brain Extractionmentioning

confidence: 99%

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Mostapha¹

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Accurate and robust extraction of brain regions using a deformable model based on radial basis functions

Cited by 29 publications

References 54 publications

Methods on Skull Stripping of MRI Head Scan Images—a Review

Methods on Skull Stripping of MRI Head Scan Images—a Review

Shape analysis of the human brain.

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

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