scholarly journals Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
He Deng ◽  
Wankai Deng ◽  
Xianping Sun ◽  
Chaohui Ye ◽  
Xin Zhou
Keyword(s):  
Brain Tumor ◽  
Mr Images ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Enhancement

BRAIN TUMOR DETECTION AND SEGMENTATION USING MULTISCALE INTUITIONISTIC FUZZY ROUGHNESS IN MR IMAGES

2019 ◽  
Vol 31 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Yogita Dubey ◽  
Milind Mushrif ◽  
Kajal Mitra
Keyword(s):  
Cerebrospinal Fluid ◽  
Brain Tumor ◽  
White Matter ◽  
Rough Set ◽  
Gray Matter ◽  
Mr Images ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Cystic Component ◽  
Brain Mr Images

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.

scholarly journals Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 696
Author(s):  
Haipeng Chen ◽  
Zeyu Xie ◽  
Yongping Huang ◽  
Di Gai
Keyword(s):  
Brain Tumor ◽  
Information Transfer ◽  
Magnetic Resonance Images ◽  
Low Noise ◽  
Similarity Measurement ◽  
Noise Sensitivity ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Membership Information ◽  
Fuzzy C Means Algorithm

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

Content-based brain tumor retrieval for MR images with joint deep and handcrafted visual features

2021 ◽  
Author(s):  
Weikang Huang ◽  
Wei Zou ◽  
Erxi Fang ◽  
Nan Hu ◽  
Jiajun Wang
Keyword(s):  
Brain Tumor ◽  
Visual Features ◽  
Mr Images

scholarly journals Brain Tumor Detection from MR Images using Image Process Techniques and Tools in Matlab Software

2021 ◽  
Vol 1 (4) ◽  
pp. 1-4
Author(s):  
Faisal Rehman ◽  
◽  
Syed Sheeraz Ali ◽  
Hamadullah Panhwar ◽  
Dr. Akhtar Hussain Phul ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Image Quality ◽  
Research Work ◽  
Cost Effective ◽  
Image Process ◽  
Medical Sciences ◽  
Mr Images ◽  
Research Areas ◽  
Tumor Identification ◽  
The Brain

In the medical era the Brain tumor is one of the most important research areas in the field of medical sciences. Researcher are trying to find the reliable and cost effective medical equipment’s for the cancer and its type for the diagnosed, especially tumor has deferent kinds but the major two type are discussed in this research paper. Which are the benign and Pre-Malignant, this research work is proposed for these factors such as the accuracy of the MRI image for the tumor identification and actual placing were taken into consideration. In this study, an algorithm is proposed to detect the brain tumor from magnetic resonance image (MRI) data simple. As enhance the image quality for the easiness the tumor treatments and diagnosed for the patients. The proposed algorithm enhances the MR image quality and detects the Brain tumor which helps the Physician to diagnose the tumor easily. As well this algorithm automatically calculates the area of tumor, size and location of the tumor where it is present for diagnostic the Patient.

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