scholarly journals Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Omid Jamshidi ◽  
Abdol Hamid Pilevar
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Fitness Function ◽  
Automatic Segmentation ◽  
Brain Images ◽  
Fuzzy C Means ◽  
Mr Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Right ◽  
Number Of Segments

Magnetic resonance imaging (MRI) segmentation is a complex issue. This paper proposes a new method for estimating the right number of segments and automatic segmentation of human normal and abnormal MR brain images. The purpose of automatic diagnosis of the segments is to find the number of divided image areas of an image according to its entropy and with correctly diagnose of the segment of an image also increased the precision of segmentation. Regarding the fact that guessing the number of image segments and the center of segments automatically requires algorithm test many states in order to solve this problem and to have a high accuracy, we used a combination of the genetic algorithm and the fuzzy c-means (FCM) method. In this method, it has been tried to change the FCM method as a fitness function for combination of it in genetic algorithm to do the image segmentation more accurately. Our experiment shows that the proposed method has a significant improvement in the accuracy of image segmentation in comparison to similar methods.

Automatic segmentation of MR brain images of preterm infants using supervised classification

NeuroImage ◽  
2015 ◽  
Vol 118 ◽  
pp. 628-641 ◽  
Author(s):  
Pim Moeskops ◽  
Manon J.N.L. Benders ◽  
Sabina M. Chiţǎ ◽  
Karina J. Kersbergen ◽  
Floris Groenendaal ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Supervised Classification ◽  
Automatic Segmentation ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

2014 ◽  
Vol 3 (1) ◽  
pp. 16 ◽  
Author(s):  
Minakshi Sharma ◽  
Saourabh Mukherjee
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Inference ◽  
Research Work ◽  
Diagnostic Technique ◽  
Clustering Methods ◽  
Inference System ◽  
Fuzzy C Means ◽  
Soft Computing Techniques

<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties.  Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques  (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus  Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of  Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.</p>

Parallel Capsule Net for Ischemic Stroke Segmentation

2019 ◽  
Author(s):  
MD Sharique ◽  
Bondi Uday Pundarikaksha ◽  
Pradeeba Sridar ◽  
R S Rama Krishnan ◽  
Ramarathnam Krishnakumar
Keyword(s):  
Image Segmentation ◽  
Ischemic Stroke ◽  
Spatial Information ◽  
Automatic Segmentation ◽  
Class Imbalance ◽  
Medical Image Segmentation ◽  
Brain Images ◽  
Data Set ◽  
Max Pooling ◽  
Mri Brain Images

AbstractStroke is one of the leading causes of disability. Segmentation of ischemic stroke could help in planning an optimal treatment. Currently, radiologists use manual segmentation, which can often be time-consuming, laborious and error-prone. Automatic segmentation of ischemic stroke in MRI brain images is a challenging problem due to its small size, multiple occurrences and the need to use multiple image modalities. In this paper, we propose a new architecture for image segmentation, called Parallel Capsule Net, which uses max pooling in every parallel pathways along with dense connections between the parallel layers. We hypothesise that the spatial information lost due to max pooling in these layers can be retrieved by the use of such dense connections. In order to combine the information encoded by the parallel layers, outputs of the layers are concatenated before upsampling. We also propose the use of a modified loss function which consists of a regional term (Generalized Dice loss + Focal Loss) and a boundary term (Boundary loss) to address the problem of class imbalance which is prevalent in medical images. We achieved a competitive Dice score of 0.754, on ISLES SISS data set, compared to a score of 0.67 reported in earlier studies. We also obtained a Dice score of 0.902 with another popular data set, ATLAS. The proposed parallel capsule net can be extended to other similar medical image segmentation problems.

Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique

2011 ◽  
Vol 3 (2) ◽  
pp. 19-33 ◽  
Author(s):  
Ahmed Kharrat ◽  
Karim Gasmi ◽  
Mohamed Ben Messaoud ◽  
Nacéra Benamrane ◽  
Mohamed Abid
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Research Work ◽  
Reduction Rate ◽  
Support Vector ◽  
Forward Selection ◽  
Classification Rate ◽  
Brain Images ◽  
Input Module ◽  
Mr Brain Images

A new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images is proposed. The proposed method uses Wavelets Transform (WT) as input module to Genetic Algorithm (GA) and Support Vector Machine (SVM). It segregates MR brain images into normal and abnormal. This contribution employs genetic algorithm for feature selection which requires much lighter computational burden in comparison with Sequential Floating Backward Selection (SFBS) and Sequential Floating Forward Selection (SFFS) methods. A percentage reduction rate of 88.63% is achieved. An excellent classification rate of 100% could be achieved using the support vector machine. The observed results are significantly better than the results reported in a previous research work employing Wavelet Transform and Support Vector Machine.

Automatic segmentation of MR brain images

1994 ◽  
Author(s):  
Nigel John ◽  
Xiaohong Li ◽  
Akmal Younis ◽  
Mansur R. Kabuka
Keyword(s):  
Automatic Segmentation ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain
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