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PCNN - Firefly Based Segmentation and Analysis of Brain MRI

B. Thamaraichelvi1

Section:Research Paper, Product Type: Journal Paper
Volume-8 , Issue-1 , Page no. 23-29, Jan-2020


Online published on Jan 31, 2020

Copyright © B. Thamaraichelvi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: B. Thamaraichelvi, “PCNN - Firefly Based Segmentation and Analysis of Brain MRI,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.23-29, 2020.

MLA Style Citation: B. Thamaraichelvi "PCNN - Firefly Based Segmentation and Analysis of Brain MRI." International Journal of Computer Sciences and Engineering 8.1 (2020): 23-29.

APA Style Citation: B. Thamaraichelvi, (2020). PCNN - Firefly Based Segmentation and Analysis of Brain MRI. International Journal of Computer Sciences and Engineering, 8(1), 23-29.

BibTex Style Citation:
author = {B. Thamaraichelvi},
title = {PCNN - Firefly Based Segmentation and Analysis of Brain MRI},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2020},
volume = {8},
Issue = {1},
month = {1},
year = {2020},
issn = {2347-2693},
pages = {23-29},
url = {},
doi = {}
publisher = {IJCSE, Indore, INDIA},

RIS Style Citation:
DO = {}
UR -
TI - PCNN - Firefly Based Segmentation and Analysis of Brain MRI
T2 - International Journal of Computer Sciences and Engineering
AU - B. Thamaraichelvi
PY - 2020
DA - 2020/01/31
SP - 23-29
IS - 1
VL - 8
SN - 2347-2693
ER -

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In this proposed method, the segmentation of brain Magnetic Resonance Images (MRI) has been carried out using Pulse Coupled Neural network (PCNN) and classification by Back Propogation Neural Network (BPNN) techniques. The proposed method includes five stages pre-processing, clustering, feature extraction, feature selection and classification. For extracting the features Non Sub-sampled Contourlet Transform (NSCT) method has been used. For feature selection optimized Fire-fly intelligence has been preferred. Finally, the selected features are given to BPNN to identify the input data either as normal or abnormal. The performance of the classifier was evaluated in terms of True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN) and the accuracy was found to be good.

Key-Words / Index Term

PCNN, NSCT, Feature extraction, feature selection. Fire-fly, MR Brain Image


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