Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

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Possibilistic Intuitionistic Fuzzy c-Means Clustering Algorithm for MRI Brain Image Segmentation

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213015500165 ◽

2015 ◽

Vol 24 (05) ◽

pp. 1550016 ◽

Cited By ~ 9

Author(s):

Hanuman Verma ◽

R. K. Agrawal

Keyword(s):

Clustering Algorithm ◽

Brain Images ◽

Brain Image ◽

Fuzzy C Means ◽

Mri Brain ◽

Intuitionistic Fuzzy ◽

Fuzzy C Means Clustering ◽

Magnetic Resonance Imaging Mri ◽

The Brain ◽

Mri Brain Images

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm for Atanassov’s intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PCM, FCM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFCM algorithm. The experimental results demonstrate that the proposed algorithm yields better result.

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Segmentation of MRI brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c-means clustering algorithm

2012 International Conference on Communications, Devices and Intelligent Systems (CODIS) ◽

10.1109/codis.2012.6422153 ◽

2012 ◽

Author(s):

Sudip Kumar Adhikari ◽

J. K. Sing ◽

D. K. Basu ◽

M. Nasipuri ◽

P. K. Saha

Keyword(s):

Clustering Algorithm ◽

Spatial Information ◽

Intensity Inhomogeneity ◽

Brain Images ◽

Fuzzy C Means ◽

Mri Brain ◽

Fuzzy C Means Clustering ◽

Mri Brain Images

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Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2012.3.1393 ◽

2014 ◽

Vol 7 (3) ◽

pp. 530 ◽

Cited By ~ 6

Author(s):

Arthanari Rajendran ◽

Raghavan Dhanasekaran

Keyword(s):

Brain Tumor ◽

Fuzzy Clustering ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Snake Model ◽

Mri Brain ◽

Mri Brain Images

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Detection and classification of brain tumours from MRI images using faster R-CNN

Tehnički glasnik ◽

10.31803/tg-20190712095507 ◽

2019 ◽

Vol 13 (4) ◽

pp. 337-342 ◽

Cited By ~ 1

Author(s):

Ercan Avşar ◽

Kerem Salçin

Keyword(s):

Deep Learning ◽

Brain Tumours ◽

Powerful Method ◽

Resonance Imaging ◽

Brain Images ◽

Mri Brain ◽

Classification Tasks ◽

Magnetic Resonance Imaging Mri ◽

Mri Brain Images

Magnetic resonance imaging (MRI) is a useful method for diagnosis of tumours in human brain. In this work, MRI images have been analysed to detect the regions containing tumour and classify these regions into three different tumour categories: meningioma, glioma, and pituitary. Deep learning is a relatively recent and powerful method for image classification tasks. Therefore, faster Region-based Convolutional Neural Networks (faster R-CNN), a deep learning method, has been utilized and implemented via TensorFlow library in this study. A publicly available dataset containing 3,064 MRI brain images (708 meningioma, 1426 glioma, 930 pituitary) of 233 patients has been used for training and testing of the classifier. It has been shown that faster R-CNN method can yield an accuracy of 91.66% which is higher than the related work using the same dataset.

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