A Survey of Brain MRI Image Segmentation Methods and the Issues Involved

Hiralal, Reshma; Menon, Hema P

doi:10.1007/978-3-319-47952-1_19

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…Hiralal and Menon [15] also provided a detailed overview about the various brain image segmentation methodologies of brain MRI images. They highlighted a very clear discussion for the selection of appropriate segmentation method for MRI brain images for the purpose of analysis and prognostication.…”

Section: Related Workmentioning

confidence: 99%

Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

et al. 2019

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Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods. INDEX TERMSNeural network (NN), deep neural network (DNN), autoencoder (AE), image classification.

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Section: Related Workmentioning

confidence: 99%

Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

et al. 2019

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“…MRI brain images taken for experimentation consists of three different categories: T1-weighted, proton density (pd)-weighted and T2-weighted. Normal images of brain are acquired from the Brainweb database [4]. In this research work, we utilise the transversal slice map, the slice thickness is 1 mm and the size is 217 x 181 pixels.…”

Section: Resultsmentioning

confidence: 99%

“…RSKFCM genetic algorithm initializes the centers of a cluster and attains the global minima of the objective function. Reshma Hiralal and Hema Menon., [4] surveyed and reviewed already existing methods for segmentation of brain MRI images techniques for detecting similarities between different tissue structures, number of homogeneous regions, image slices and orientation present in the brain image. Kiran et al, [3] proposed an algorithm which utilizes Variational Mode Decomposition (VMD) for hyper spectral data which included the process of classification, which was constructed upon sparse representation.…”

Section: Introductionmentioning

confidence: 99%

An enhanced variational level set method for MRI brain image segmentation using IFCM clustering and LBM

Duth¹,

Kulkarni²

2018

IJET

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Today's technological advances in medical imaging have given rise to efficient diagnostic procedures. Segmentation identifies and defines individual objects with various attributes such as size, shape, texture, spatial location, contrast, brightness, noise, and context. Deformable segmentation methods are Active contours, which are used to match and track images of an atomic structure by determining constraints derived from the image data. Level set method is an integral part of active contour family, considerable work towards level set methods has identified two main disadvantages i.e., initialization of controlling parameters and time complexity. In this paper, the methodology employed proposes an enhanced Variational level set methodology for Magnetic Resonance (MR) brain image segmentation with heterogeneous intensity. Core concept of IFCM is based on Intuitionistic fuzzy set. Both the values of membership and non membership values for the purpose of labelling are utilized together. As the result of experimentation reveals the efficiency of the recommended IFCM algorithm and Lattice Boltzmann Method (LBM) to overcome the drawbacks of Level Set methods by using the energy function to reduce the processing time which addresses the time complexity issue. The proposed system combines of both IFCM and LBM to form a novel approach. The system is tested on a large set of MRI brain images, extensive research and experiments were carried over on the standard dataset and the results are found to be improved in identification of tumor size detection with respect to time complexity.

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“…This causes the system to deplete important set of clinical information which is predominantly required for making decision of the presence of abnormalities present in the brain MRI image. Apart from this, existing segmentation process are too straight forward and less work are found to be extensive [8,9]. It has been seen that there has been not much focus toward employing pre-processing techniques much for brain MRI images, which is highly essential.…”

Section: Introductionmentioning

confidence: 99%

An optimized approach for extensive segmentation and classification of brain MRI

Harish¹,

Ahammed²

2020

IJECE

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With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.

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A Survey of Brain MRI Image Segmentation Methods and the Issues Involved

Cited by 18 publications

References 30 publications

Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

An enhanced variational level set method for MRI brain image segmentation using IFCM clustering and LBM

An optimized approach for extensive segmentation and classification of brain MRI

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