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

2016 ◽  
pp. 81-104 ◽  
Author(s):  
Eman A. Abdel Maksoud ◽  
Mohammed Elmogy
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Clustering Techniques ◽  
Hybrid Clustering

BRAIN TUMOR SEGMENTATION BASED ON SUPERPIXELS AND HYBRID CLUSTERING WITH FAST GUIDED FILTER

2020 ◽  
Vol 20 (06) ◽  
pp. 2050032
Author(s):  
CHONG ZHANG ◽  
XUANJING SHEN ◽  
HAIPENG CHEN
Keyword(s):  
Brain Tumor ◽  
Clustering Algorithm ◽  
Guided Filter ◽  
Gaussian Kernel ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Hybrid Clustering ◽  
Simple Linear Iterative Clustering ◽  
The Stability ◽  
Sensitivity Specificity

Brain tumor segmentation from magnetic resonance (MR) image is vital for both the diagnosis and treatment of brain cancers. To alleviate noise sensitivity and improve stability of segmentation, an effective hybrid clustering algorithm combined with fast guided filter is proposed for brain tumor segmentation in this paper. Preprocessing is performed using adaptive Wiener filtering combined with a fast guided filter. Then simple linear iterative clustering (SLIC) is utilized for pre-segmentation to effectively remove scatter. During the clustering, K-means[Formula: see text] and Gaussian kernel-based fuzzy C-means (K[Formula: see text]GKFCM) clustering algorithm are combined to segment, and the fast-guided filter is introduced into the clustering. The proposed algorithm not only improves the robustness of the algorithm to noise, but also improves the stability of the segmentation. In addition, the proposed algorithm is compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity and recall.

scholarly journals Brain tumor segmentation based on a hybrid clustering technique

2015 ◽  
Vol 16 (1) ◽  
pp. 71-81 ◽  
Author(s):  
Eman Abdel-Maksoud ◽  
Mohammed Elmogy ◽  
Rashid Al-Awadi
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Hybrid Clustering ◽  
Clustering Technique

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge

2017 ◽  
Vol 16 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Tianming Zhan ◽  
Yi Chen ◽  
Xunning Hong ◽  
Zhenyu Lu ◽  
Yunjie Chen
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Brain Tumor Segmentation
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