Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

Loyek, Christian; Woermann, Friedrich G.; Nattkemper, Tim Wilhelm

doi:10.1007/978-3-540-78640-5_87

Cited by 15 publications

(13 citation statements)

References 8 publications

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“…For example, voxels not satisfying the criteria of GMM normal tissue models derived from the Expectation Maximization (EM) algorithm (22) are detected as lesions. MRI based automated lesion detection in other neurological diseases, such as multiple sclerosis (15, 23–25), focal cortical dysplasias (26, 27) and ischemic stroke (11) has generated great interest. There are no studies that have examined the use of automated detection of neonatal ischemic injury (the focus of this work).…”

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confidence: 99%

Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury

Ghosh¹,

Recker²,

Shah³

et al. 2011

Magnetic Resonance Imaging

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Purpose: To develop and compare an automated detection system for ischemic lesions in a neonatal model of bilateral carotid artery occlusion with hypoxia (BCAO-H) from T2 weighted MRI (T2WI) to the currently used ''gold standard'' of manual segmentation.Materials and Methods: Forty-three P10 BCAO-H rat pups and 8 controls underwent T2WI at 1 day and 28 days. A computational imaging method, Hierarchical Region Splitting (HRS), was developed to automatically and rapidly detect and quantify 3D lesion and normal appearing brain matter (NABM) volumes.Results: HRS quantified lesion and NABM volumes within 15 s in comparison to 3 h for its manual counterpart, with a high correlation for injury (r 2 ¼ 0. 95; P ¼ 8.6 Â 10 À7 ) and NABM (r 2 ¼ 0. 92; P ¼ 1.4 Â 10 À22 ). Average lesion volumes for mild, moderate, and severe injuries were 3.85%, 28.85%, and 52.98% for HRS and 0.51%, 24.22%, and 48.74% for manual detection. Lesion volumes and locations were similar for both methods (sensitivity: 0.82, specificity: 0.86, and similarity: 1.47). Conclusion:HRS is an accurate, objective, and rapid method to quantify injury evolution in neonatal hypoxic ischemic injury models.

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confidence: 99%

Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury

Ghosh¹,

Recker²,

Shah³

et al. 2011

Magnetic Resonance Imaging

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“…Some computational methods have been developed for other diseases such as multiple sclerosis (MS) [ 19,20 ] , focal cortical dysplasias (FCD) [ 21,22 ] , and white matter lesions (WML) [ 23 ] . 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 ] .…”

Section: Current Computational Approaches For Lesion Detectionmentioning

confidence: 99%

“…First, computationally intensive mathematical models are generally used that hinder rapid estimation of the size and nature of the lesion which is time sensitive when used clinically [ 19,21,25,30,39 ] . Second, many current methods use anatomical brain atlases and a priori probabilistic tissue models [ 23 ] to facilitate lesion detection and reject outliers [ 19,33 ] .…”

Section: Recently Emerging Approaches For Automated Lesion Detectionmentioning

confidence: 99%

“…SVM attempts to maximize the "functional margin" of the hyperplane or a separation that maximizes accuracy and distance between the separating plane and most data points [ 46 ] , leading to improved tissue identifi cation. Recently, SVM has been used to detect FCD from texture features derived from MRI [ 21 ] . Initially, gray matter was found by registering to an atlas and gray matter textures were derived from statistical gray level cooccurrence and "run length" features (i.e., for how many pixels/voxels the same classifi cation continues in different image directions).…”

Section: Support Vector Machinementioning

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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“…Recently, it has been shown that the determination of structural and volumetric asymmetries in the human brain from MRI provides critical data for the diagnosis of abnormality [2]. Christian Loyek et al carried out the work in epilepsy prediction and developed a model for predicting focal cortical dysplasia lesions, which is a frequent cause of medically refractory partial epilepsy, in MRI using support vector machine [3]. M.C.Clarke et al developed a method for abnormal MRI volume identification with slice segmentation using Fuzzy C-means algorithm [4].…”

Section: Introductionmentioning

confidence: 99%

Support vector machine based epilepsy prediction using textural features of MRI

Sujitha¹,

Sivagami²,

Vijaya³

2010

Procedia Computer Science

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Epilepsy is a disorder of the central nervous system, specifically the brain. It is a neurological malfunction affecting about 1% of the population and is the third most common neurological disorder following rheumatic heart disease and Alzheimer's disease, but it imposes higher costs on society. Magnetic Resonance Imaging (MRI) is one of the most common diagnostic tests used for patients for epilepsy prediction. Shortage of radiologists and the large volume of MRI scan images that need to be analyzed may lead to labor intensive, expensive and inaccurate prediction. Hence there is a need to generate an efficient prediction model for making a correct diagnosis of epilepsy and accurate prediction of its type. This paper describes the modeling of epilepsy prediction using Support Vector Machines (SVM), a machine learning algorithm. The prediction model has been generated by training the support vector machine with descriptive features derived from MRI data of 350 patients and observed that the SVM based model with a Radial Basis Function (RBF) kernel produces 93.87% of prediction accuracy.

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Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

Cited by 15 publications

References 8 publications

Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury

Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury

Computational Analysis: A Bridge to Translational Stroke Treatment

Support vector machine based epilepsy prediction using textural features of MRI

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