Comparison of Active Contour and Active Shape Approaches for Corpus Callosum Segmentation

Adiva, Enkhbolor; Izmantoko, Yonny S.; Choi, Heung‐Kook

doi:10.9717/kmms.2013.16.9.1018

“…Statistical models (SM) have become widely used in the field of computer vision and medical image segmentation over the past decade [26,[45][46][47][48][49][50][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88]. Basically, SMs use a priori shape information to learn the variation from a suitably annotated training set, and constrain the search space to only plausible instances defined by the trained model.…”

Section: Statistical Modelsmentioning

confidence: 99%

“…ASM has been widely used for segmentation in medical imaging [73], including segmentation of subcortical structures on brain [47,48,[77][78][79][80][81][82][83][84]. It is based on a statistical shape model (SSM) to constrain the detected organ boundary to plausible shapes (i.e.…”

Section: Active Shape Modelmentioning

confidence: 99%

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Dolz

¹

,

Massoptier²,

Vermandel

³

2015

IRBM

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This work covers the current state of the art with regard to approaches to segment subcortical brain structures. A huge range of diverse methods have been presented in the literature during the last decade to segment not only one or a constrained number of structures, but also a complete set of these subcortical regions. Special attention has been paid to atlas based segmentation methods, statistical models and deformable models for this purpose. More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem. These methods are presented in this work, and their advantages and drawbacks are further discussed. Although these methods have proved to perform well, their use is often limited to those situations where either there are no lesions in the brain or the presence of lesions does not highly vary the brain anatomy. Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice by some clinical applications, such as radiotherapy or radiosurgery.

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“…A research has been carried out to find the most discriminating CC features between the two groups (Hensel et al, 1990). Since the accurate classification depends upon the extracted features and quality of CC segmentation, various algorithms have been proposed for 2D and 3D segmentation CC (Adiya et al, 2013;Li et al, 2013;Lundervold et al, 1999). Morphological and textural parameters of CC are found to be the discriminating factors for classifying dementia patients from normal patients (Haralick, 1979;Hensel et al, 1990).…”

Section: Introductionmentioning

confidence: 99%

Classification of Brain MR Images Using Corpus Callosum Shape Measurements

Bhalerao

¹

,

Sampathila

²

2015

International Journal of Biomedical and Clinical Engineering

1

0

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The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.

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Classification of Brain MR Images Using Corpus Callosum Shape Measurements

Bhalerao

¹

,

Sampathila

²

2017

Medical Imaging

0

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The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.

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Comparison of Active Contour and Active Shape Approaches for Corpus Callosum Segmentation

Cited by 7 publications

References 28 publications

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Classification of Brain MR Images Using Corpus Callosum Shape Measurements

Classification of Brain MR Images Using Corpus Callosum Shape Measurements

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