A fully automated hybrid methodology using Cuckoo-based fuzzy clustering technique for magnetic resonance brain image segmentation

2017 ◽  
Vol 27 (4) ◽  
pp. 317-332 ◽  
Author(s):  
Saravanan Alagarsamy ◽  
Kartheeban Kamatchi ◽  
Vishnuvarthanan Govindaraj ◽  
Arunprasath Thiyagarajan
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Fuzzy Clustering ◽  
Brain Image ◽  
Clustering Technique ◽  
Hybrid Methodology ◽  
Brain Image Segmentation

An Automatic Magnetic Resonance Brain Image Segmentation Method Using a Multitask Weighted Fuzzy Clustering Algorithm

2021 ◽  
Vol 11 (8) ◽  
pp. 2177-2183
Author(s):  
Lei Hua ◽  
Jing Xue ◽  
Kaijian Xia ◽  
Leyuan Zhou ◽  
Pengjiang Qian ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Brain Images ◽  
Brain Image ◽  
Fuzzy Clustering Algorithm ◽  
Brain Image Segmentation

In clinical assisted diagnosis, it is an important way to obtain information with the help of medical images. Qualitative and quantitative analysis of brain tissue has become a research hotspot for brain diseases. Therefore, image segmentation technology is an indispensable link in medical image analysis. Due to the defects such as ambiguity, complexity, gray-scale unevenness, partial volume effect in magnetic resonance brain images, it is essential to improve the segmentation performance of classical algorithms in medical images. In this paper, multitasking and weighted fuzzy clustering algorithm are combined as a new algorithm (MT-WFCM) for MRI brain image segmentation. The proposed MT-WFCM algorithm improve the clustering performance of all tasks through common information between different magnetic resonance brain images with correlation. Besides, the difference between MT-WFCM and MT-FCM is that task weights are added to avoid negative effects between tasks in the segmentation process. According to five different comparative experiments, the MT-WFCM algorithm can mine the cooperative relationship among each task and the characteristics of each task effectively. In magnetic resonance image (MRI) segmentation, multi-task weighted fuzzy c-means clustering method can make up for the shortcomings of single-task clustering algorithm, strengthen the relationship between tasks, and get more accurate segmentation results.

Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation

2012 ◽  
Vol 22 (5) ◽  
pp. 1013-1022 ◽  
Author(s):  
D. Jude Hemanth ◽  
C. Kezi Selva Vijila ◽  
A. Immanuel Selvakumar ◽  
J. Anitha
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Distance Metric ◽  
Fuzzy Algorithm ◽  
Brain Image ◽  
Brain Image Segmentation

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

2017 ◽  
pp. 115-130
Author(s):  
Vijay Kumar ◽  
Jitender Kumar Chhabra ◽  
Dinesh Kumar
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Clustering Technique ◽  
Mri Brain ◽  
Gravitational Search ◽  
The Brain ◽  
Mri Brain Image ◽  
Brain Tissues ◽  
Brain Image Segmentation

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

Review of Set Theoretic Approaches to Magnetic Resonance Brain Image Segmentation

2019 ◽  
pp. 1-18 ◽  
Author(s):  
Anupama Namburu ◽  
Samayamantula Srinivas Kumar ◽  
Edara Srinivasa Reddy
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Brain Image ◽  
Brain Image Segmentation

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

2014 ◽  
pp. 313-326 ◽  
Author(s):  
Vijay Kumar ◽  
Jitender Kumar Chhabra ◽  
Dinesh Kumar
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Clustering Technique ◽  
Mri Brain ◽  
Gravitational Search ◽  
The Brain ◽  
Mri Brain Image ◽  
Brain Tissues ◽  
Brain Image Segmentation

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

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