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Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

  • S.I. : Neuro, fuzzy and their Hybridization
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Abstract

This paper presents an efficient skull strip** method to improve the decision-making process. Extended Weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu’s and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%).

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Abbreviations

EWF:

Extended Weiner filter

LXLOA:

Laplacian lion optimization algorithm

WOA:

Whale optimization algorithm

APSO:

Adaptive particle swarm optimization

DE:

Differential evolution

LOA:

Lion optimization algorithm

ACSA:

Adaptive cuckoo search algorithm

PSO:

Particle swarm optimization

GWO:

Grey wolf optimization

CSA:

Cuckoo search algorithm

CSO:

Cat swarm optimization

CNN:

Convolutional neural network

IBSR:

Brain segmentation repository

MRI:

Magnetic resonance imaging

CT:

Computed tomography

PSRN:

Peak signal-to-noise ratio

SSIM:

Structural similarity index measure

RMSE:

Root mean square error

SVM:

Support vector machine

ANN:

Artificial neural network

LDA:

Linear discriminant analysis

FKM:

Fuzzy weighted K-mean

WHO:

World Health Organization

3-D:

3-Dimensional

CT:

Computed tomography

LB:

Lower bound

UB:

Upper bound

DIM:

Dimension

GLCM:

Grey level co-occurrence matrices

GLDM:

Grey level difference matrix

CEC:

Congress on evolutionary computation

\(K(x,y)\) :

Filter

U(d, h):

Fourier transform of PSF (point spread function)

\({P}_{s}\left(d,h\right)\) :

Power spectrum of the processed signal process

\({P}_{n}\left(d,h\right)\) :

Power spectrum of processed noise

\(SI\) :

Dispersion index

\(\sigma\) :

Standard deviation

\(\mu\) :

Mean

EWF (x,y):

Extended wiener filter

\({M}_{final}\) :

Fitness value

\(\alpha\),\(\beta\) :

Random values ranging from 0 to 1

\({M}_{Otsu}\) :

Otsu’s function

\({M}_{Tsallis entropy}\) :

Tsallis entropy

\({l}_{i}\) :

Laplacian distributed random ``number

\(w\) :

Location

\(q\) :

Scale parameter

\({u}_{i}\),\({v}_{i}\) :

Distributed random numbers having range [0, 1]

\({\mathrm{New}\_\mathrm{Cub}}_{M}\) :

Offspring (New cube)

\({x}_{male}^{i}\) :

Male in pride

\({x}_{female}^{i}\) :

Female in pride

U:

Universal function

K(x,y) :

Factor of features

\({s}_{xk}\) :

Membership function showing the fuzzy cluster

Wfb:

Fuzzy weighted k-means

\({y}_{ie}\) :

Factor

\({c}_{ke}\) :

Weighted mean

\({f}_{ek}\) :

Weight of feature e for cluster k.

\({m}_{xy}\) :

Weighted mean

\({g}_{y}\) :

Sample of data belonging to y

\({n}_{x}\) :

Count of data points reside in x

g:

Relative distance from the cluster

m:

Fuzzifier function

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    Authors and Affiliations

    1. Department of Information Technology, KIET Group of Institution, Ghaziabad, India

      Surbhi Vijh

    2. Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, England

      Hari Mohan Pandey

    3. V R Techniche Consultants Pvt Ltd, Noida, India

      Prashant Gaurav

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    1. Surbhi Vijh
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    Surbhi Vijh and Hari Mohan Pandey equally contributed to this work.

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    Vijh, S., Pandey, H.M. & Gaurav, P. Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis. Neural Comput & Applic 35, 7315–7338 (2023). https://doi.org/10.1007/s00521-021-06709-w

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