scholarly journals Performance of GLCM Algorithm for Extracting Features to Differentiate Normal and Abnormal Brain Images

2021 ◽  
Vol 1082 (1) ◽  
pp. 012011
Author(s):  
Zul Indra ◽  
Yessi Jusman
Keyword(s):  
2018 ◽  
Vol 7 (2.24) ◽  
pp. 536 ◽  
Author(s):  
Yepuganti Karuna ◽  
Saritha Saladi ◽  
Budhaditya Bhattacharyya

Distinct algorithms were developed to segment the MRI images, to satisfy the accuracy in segmenting the regions of the brain. In this paper, we proposed a novel methodology for segmenting the MRI brain images using the clustering techniques. The Modified Fuzzy C-Means (MFCM) algorithm is pooled with the Artificial Bee Colony (ABC) algorithm after denoising images, features are extracted using Principal Component Analysis (PCA) for better results of segmentation. This improves the ability to extract the regions (cluster centres) and cells in the normal and abnormal brain MRI images. The comparative analysis of proposed methodology with existing FCM, ABC algorithms is evaluated in terms of Minkowski score. The proposed MFCM-ABC method is more robust and efficient to hostile noise in images when compared to existing FCM and ABC methods.                                                                                                                                


2019 ◽  
Vol 5 (1) ◽  
pp. 37-42
Author(s):  
Fajar Akbar ◽  
Amin Nur Rais ◽  
Irwan Agus Sobari ◽  
Robi Aziz Zuama ◽  
Biktra Rudiarto

The brain in humans becomes part of the central nervous system of the human body. The use of imaging with MRI is one that can be used as a first step to detect parts of the human brain. The imaging step is the first step in diagnosing brain tumor. By performing feature extraction, which aims to process the classification of brain tumors, between normal and abnormal brain images using the naive Bayes method. Obtained 41 images which then became 39 datasets. Feature extraction results with 2 classes, normal as many as 20 data and abnormal data 19. The calculation results obtained the value of the normal class of 0.513 and the abnormal class of 0.487 the value of the calculation accuracy of 84.17%.


Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan

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.


2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  

Author(s):  
Katarzyna Curzytek ◽  
Monika Leśkiewicz

AbstractSince affective disorders are considered to be underlain by the immune system malfunction, an important role in their pathophysiology is assigned to the proinflammatory mediators. Recently, chemokines, the group of chemotactic cytokines, have become a focus for basic and clinical scientists in the context of the development and treatment of brain diseases. Among them, chemokine CCL2 and its main receptor CCR2 have become candidate mediators of abnormal brain-immune system dialogue in depression. Besides the chemotactic activity, the CCL2-CCR2 axis is involved in various neurobiological processes, neurogenesis, neurotransmission, neuroinflammation, neurodegeneration, as well as neuroregeneration. Given the range of immunomodulatory possibilities that the CCL2-CCR2 pair can exert on the nervous system, its proinflammatory properties were initially thought to be a major contributor to the development of depressive disorders. However, further research suggests that the malfunctions of the nervous system are rather associated with impaired homeostatic properties manifested by the CCL2-CCR2 dyad dysfunctions. This review aims to present literature data on the action of the CCL2-CCR2 axis in the central nervous system under physiological and pathological conditions, as well as the contribution of this ligand-receptor system to the processes underlying affective disorders. Additionally, this article draws attention to the importance of the CCL2-CRR2 pathway as a potential pharmacological target with antidepressant potential.


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