3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI

Maksoud, Eman A. Abdel; Elmogy, Mohammed

doi:10.1007/978-3-319-33793-7_4

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…Sobel method along close contour algorithm has been proposed and the authors claim superior results over conventional methods [7].Canny edge detectors along with fuzzy c-means clustering has been used in [8] and achieved around 10 to 15% more accurate results for some images. An adaptive region growing method based on gradients and variances with anisotropic filter for pre-processing has been proposed and obtained good results [9] Hybrid techniques has been proposed by many researchers for better segmentation results [10] , [11].…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

Rani P*,

Harsha,

Singh

et al. 2019

IJRTE

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Medical image processing is an important task in current scenario as more and more humans are diagnosed with various medical issues. Brain tumor (BT) is one of the problems that is increasing at a rapid rate and its early detection is important in increasing the survival rate of humans. Detection of tumor from Magnetic Resonance Image (MRI) of brain is very difficult when done manually and also time consuming. Further the tumors assume different shapes and may be present in any portion of the brain. Hence identification of the tumor poses an important task in the lives of human and it is necessary to identify its exact position in the brain and the affected regions. The proposed algorithm makes use of deep learning concepts for automatic segmentation of the tumor from the MRI brain images. The algorithm is implemented using MATLAB and an accuracy of 99.1% is achieved.

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Section: Related Workmentioning

confidence: 99%

Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

Rani P*,

Harsha,

Singh

et al. 2019

IJRTE

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“…This field still required better algorithms to obtain satisfactory results in order to plan the best treatments. There are thus research studies in this field, such as Myronenko, Vijay et al, and Abdel Maksoud et al Unlike these works, our proposed method is characterized by a reduced execution time with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

3D brain tumor segmentation in MRI images based on a modified PSO technique

Gtifa

Hamdaoui

Sakly

2019

Int J Imaging Syst Tech

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Three‐dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional‐order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1‐C modality.

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“…Many efforts were made in recent years to develop human-free intervention methods that can achieve similar results to those obtained by physicians. Numerous techniques have been used for this purpose, which can be grouped in 2 classes, i.e., supervised [1][2][3][4][5][6] and unsupervised methods [7][8][9][10][11]. In Refs.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the cooperation between Particle Swarm Optimization and K-means was suggested in Ref. [10] for segmenting successive slices of 3D MR images. Hamamci et al [11] have used the tumor probability map that is constructed from cellular automata states to achieve a smooth level set evolution towards brain tumor boundaries delimited in MR images.…”

Section: Introductionmentioning

confidence: 99%

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation

Ilunga-Mbuyamba

Aviña-Cervantes

Cepeda-Negrete

et al. 2017

Computers in Biology and Medicine

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A. y Chalopi, Claire (2017). Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Computers in Biology and

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3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI

Cited by 9 publications

References 20 publications

Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

3D brain tumor segmentation in MRI images based on a modified PSO technique

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation

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