This paper surveys the extensive usage of pulse coupled neural networks. The visual cortex system of mammalians was the backbone for the development of pulse coupled neural network. PCNN (Pulse Coupled Neural Networks) is unique from other techniques due to its synchronous pulsed output, adjustable threshold and controllable parameters. is Hence the uniqueness of this network utilized in the fields of image processing. The basic model of PCNN and the consecutive changes implemented, to strengthen the pulse coupled neural network are discussed initially. Then the applications of PCNN are broadly discussed. The other miscellaneous applications utilizing pulse coupled neural networks are thrown light in the last section. © 2013 Elsevier Ltd. All rights reserved.

Monica Subashini M

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

Sahoo S.K.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Expert Systems with Applications

Brain tumor grade identification is an invasive technique and clinicians rely on biopsy and spinal tap method. The proposed method takes an effort to develop a non-invasive method for the tumor grade (Low/High) identification using magnetic resonant images. The process involves preprocessing, image segmentation, tumor isolation, feature extraction, feature selection and classification. An analysis on the performance of the segmentation techniques, feature extraction methods, automatic feature selection (SFLA) and constructed classifiers (support vector machines, learning vector quantization and Naives Bayes) is done on the basis of accuracy, efficiency and elapsed time. This analysis motivates towards the accurate determination of tumor grade from MR images instead of depending on magnetic resonant spectroscopy and biopsy. Fuzzy c-means segmentation outperformed other segmentation techniques, shape and size based textural feature promoted the demarcation of tumor grades, Naive Bayes classifier succeeded in terms of efficiency, error and elapse time when compared with SVM and LVQ. The study was carried out with 200 images consisting training set (164 images) and testing set (36 images). The results revealed that the system is robust and accurate (91%), consumed less time in grade identification, an alternative for biopsy and MRS in the brain tumor grade identification diagnosis procedure. © 2015 Elsevier Ltd. All rights reserved.

A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques

The proposed method predicts the grade of astrocytoma based on certain features extracted from magnetic resonant images. MR brain images of three views namely Axial, Sagittal and Coronal are considered for perfect grade evaluation of astrocytoma. Gray level co-occurrence matrix is utilized for feature extraction. The extracted features are subjected to classification through a classifier which predicts the grade. Fuzzy logic classifier and K-Clustering network are the most used classifiers for prediction of grades among many available. The performances of the techniques are evaluated through discussions with neuroradiologist to find their accuracies. A modified K-Clustering network is developed to give a better diagnosis for predicting the grades of astrocytoma. © Springer India 2015.

Lecture Notes in Electrical Engineering Power Electronics and Renewable Energy Systems

A Grade Prediction Methodology for Astrocytoma Using Modified K-Clustering Network

Soft Computing

A coarse-to-fine strategy for iterative segmentation using simplified pulse-coupled neural network

Optics and Lasers in Engineering

Novel image fusion method based on adaptive pulse coupled neural network and discrete multi-parameter fractional random transform

Optik

A medical image fusion method based on energy classification of BEMD components

Neurocomputing

Automatic iterative algorithm for image segmentation using a modified pulse-coupled neural network

A novel algorithm of remote sensing image fusion based on Shearlets and PCNN

Pulse coupled neural networks and its applications

Journal	Data powered by TypesetExpert Systems with Applications
Publisher	Data powered by TypesetElsevier BV
ISSN	0957-4174
Open Access	No