An active contour model for brain magnetic resonance image segmentation based on multiple descriptors

Chen, Hong; Yu, Xiaoqing; Wu, Chengdong; Jian, Wu

doi:10.1177/1729881418783413

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…The proposed method can complete segmentation and bias correction for brain MR images. Experimental results show our method meets all the four conditions of accurate segmentation of brain tissue: the full description of gray value distribution, robust to serious noise, multiphase segmentation, and bias field estimation [17].…”

Section: Active Contour With Convolutional Neural Networkmentioning

confidence: 79%

Medical Image Segmentation with active contour and optimization Techniques: Survey

Hussein,

Mosa

2022

J. Al-Qadisiyah Comp. Sci. Math.

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The principal aim is to improve medical diagnosis through segmented images. Thus, medical image segmentation become one of the key technologies in computer-aided diagnosis. Active contours, or snakes, have been widely used for image segmentation purposes. However, high noise sensitivity and poor performance over weak edges are the most acute issues that hinder the segmentation accuracy of these curves, particularly in medical images. To overcome these issues, a novel external force that integrates gradient vector flow (GVF) field forces and the traditional snake function is proposed in this research. In addition, a novel technique is applied to limit the boundary of the initial contour by set four initial points around the medical issue and then connecting them by polynomial curves. Moreover, the positive effect of Particle Swarm Optimization (PSO) on calculating the final active contour area and its percentage to the entire image area is proved in this work.

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Section: Active Contour With Convolutional Neural Networkmentioning

confidence: 79%

Medical Image Segmentation with active contour and optimization Techniques: Survey

Hussein,

Mosa

2022

J. Al-Qadisiyah Comp. Sci. Math.

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“…They use internal and external forces to delimit the limits of objects and thus distort images. We could distinguish parametric models (active contours or snake) [101][102][103][104][105] and geometric models [106]. They are robust to noise and parasitic edges thank to their ability to generate closed parametric surfaces or curves.…”

Section: The Form-based Approachmentioning

confidence: 99%

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

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Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

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Semantic segmentation of the multiform proximal femur and femoral head bones with the deep convolutional neural networks in low quality MRI sections acquired in different MRI protocols

Memiş

Varlı

Bilgili

2020

Computerized Medical Imaging and Graphics

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An active contour model for brain magnetic resonance image segmentation based on multiple descriptors

Cited by 4 publications

References 21 publications

Medical Image Segmentation with active contour and optimization Techniques: Survey

Medical Image Segmentation with active contour and optimization Techniques: Survey

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Semantic segmentation of the multiform proximal femur and femoral head bones with the deep convolutional neural networks in low quality MRI sections acquired in different MRI protocols

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