Medical Image Segmentation Using Artificial Neural Networks

Moghaddam, Mostafa Jabarouti; Soltanian‐Zadeh, Hamid

doi:10.5772/16103

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…Probabilistic neural network (PNN) along with wavelet transform is used for classification of tumors from CT images and then second order statistical features were obtained from ROI of segmented image (Depeursinge et al, 2010). The authors (Moghaddam and Zadeh, 2011) discussed about various possibilities of medical image segmentation using artificial neural networks that includes: feedback (i.e. Hopfield, cellular etc.)…”

Section: Related Workmentioning

confidence: 99%

Hybrid Data Mining Approach for Image Segmentation Based Classification

Panda

Hassanien

Abraham

2016

International Journal of Rough Sets and Data Analysis

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Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

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Section: Related Workmentioning

confidence: 99%

Hybrid Data Mining Approach for Image Segmentation Based Classification

Panda

Hassanien

Abraham

2016

International Journal of Rough Sets and Data Analysis

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“…[14] Artifical Neural network image enhancement, segmentation, registration, feature extraction, and object recognition Appropriate for real time application and for medical image segmentation applications.…”

Section: Specification Of Contours Edgementioning

confidence: 99%

A methodological survey of image segmentation using soft computing techniques

Singh

Gupta

Saini

2015

2015 International Conference on Advances in Computer Engineering and Applications

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This paper presents a state of art review of image segmentation using soft computing techniques. Some of these techniques are explained by in what ways they similar to GAs and in what ways they differ. Different soft techniques of implementing image segmentation have been reviewed in this paper. Finally, summaries and review of research work on image segmentation using soft computing techniques has been represented.

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“…Reduction of the computation time of Gas is another important consideration in medical image analysis that is time consuming nature [4][12] [14]. This paper [13] proposed several methods for medical image segmentation based on artificial neural networks. The networks were categorized into feedback and feed-forward networks.…”

Section: IImentioning

confidence: 99%

Cardiac image segmentation using Simulated Genetic algorithm

Singh

Misra

Varsha³

2015

2015 International Conference on Advances in Computer Engineering and Applications

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Cardiac Image Segmentation poses many challenges arising from the large variation between different sequences of images. As we know that Segmentation of moving objects in image sequences is more difficult .In the present paper we use Simulated Genetic Algorithm for Cardiac Image Segmentation to deal with these challenges. We propose an algorithm for segmentation of medical image sequences based on Simulated Genetic Algorithm which uses K-mean clustering in the feature vector space. We use two-dimensional feature vector for clustering in the feature space. Experiments on Cardiac images have given the satisfactory results.

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Medical Image Segmentation Using Artificial Neural Networks

Cited by 9 publications

References 44 publications

Hybrid Data Mining Approach for Image Segmentation Based Classification

Hybrid Data Mining Approach for Image Segmentation Based Classification

A methodological survey of image segmentation using soft computing techniques

Cardiac image segmentation using Simulated Genetic algorithm

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