A new local tetra pattern in composite planes (LTcP) technique for classifying brain tumors using partial least squares and super-pixel segmentation

Chaturvedi, Ravi Prakash; Mishra, Annu; Ansari, Mohd Dilshad; Kushwaha, Ajay Shriram; Mittal, Prakhar; Singh, Rajneesh Kumar

A new local tetra pattern in composite planes (LTcP) technique for classifying brain tumors using partial least squares and super-pixel segmentation

International Journal on Smart Sensing and Intelligent Systems

Volume 18 (2025): Issue 1 (January 2025)

By:

Ravi Prakash Chaturvedi, Annu Mishra, Mohd Dilshad Ansari, Ajay Shriram Kushwaha, Prakhar Mittal and Rajneesh Kumar Singh

Open Access

|Oct 2025

Figures & Tables

Architecture of an LTrP via a Fourier descriptor [6].

Overall architecture of the proposed methodology.

Illustration (A) Segmented image for the T1 MRI image (B) Segmented image for the T2 MRI image. (C) Segmented image for the FLAIR MRI image. MRI, magnetic resonance imaging.

Use of the LTcP descriptor for feature extraction.

Illustration of (A) local features from T1 MR images and (B) local features from T2 MR images. (C) Local features from the FLAIR MR image.

Feature fusion result after applying PLS for (A) T1 MRI images, (B) T2 MRI images, and (C) FLAIR MRI images. MRI, magnetic resonance imaging; PLS, partial least squares.

Final accuracy chart and computational time chart based on the proposed method with different classifiers for BraTS2019 with 35 fused features via PLS. PLS, partial least squares.

ROC curve for the proposed method on BraTS2019.

Segmented results on the BraTS19 dataset.

Accuracy chart of the proposed method with different classifiers for BraTS2018 with 35 fused features via PLS. PLS, partial least squares.

ROC curve for the proposed method on BraTS2018.

Segmented results on the BraTS18 dataset.

Comparison of results based on different classifiers using fused features of image (brats2018)

Number of features used	Technique	Feature extraction	Fusion method	Accuracy	Computational time (s)
5	SVM	LTcP	PLS	81.5	82.3
5	Naïve Bayes			81.7	81.9
5	Softmax			82.2	83.8
5	Decision tree			81.8	82.6
10	SVM	LTcP	PLS	83.5	85.3
10	Naïve Bayes			84.3	82.1
10	Softmax			80.5	83.8
10	Decision tree			81.6	85.4
15	SVM	LTcP	PLS	84.7	88.9
15	Naïve Bayes			84.6	87.9
15	Softmax			82.3	85.4
15	Decision tree			86.5	86.6
20	SVM	LTcP	PLS	85.6	91.7
20	Naïve Bayes			84.6	92.6
20	Softmax			81.3	97.8
20	Decision tree			82.5	99.3
25	SVM	LTcP	PLS	85.2	92.4
25	Naïve Bayes			83.5	98.7
25	Softmax			81.2	101.4
25	Decision tree			83.7	109
30	SVM	LTcP	PLS	85.2	92.6
30	Naïve Bayes			85.6	99.4
30	Softmax			81.2	104.8
30	Decision tree			83.6	111.3
35	SVM	LTcp	PLS	85.8	94.4
35	Naïve Bayes			84.6	98.7
35	Softmax			85.8	105.3
35	Decision tree			84.6	118.2

Numbers of features extracted using the proposed method

Method (features extraction)	Number of features
LTcP (proposed method)	42
LTrP [18]	35
LBP [15]	10
GLCM [26]	19
GLRM [27]	7

Comparison of results based on different classifiers using various numbers of fused features of the image (brats2019)

Number of features used	Technique	Feature extraction	Fusion method	Accuracy	Computational time (s)
−5	SVM	LTcP	PLS	80.2	82.3
5	Naïve Bayes			79.6	81.9
5	Softmax			79.2	83.8
5	Decision tree			75	82.6
10	SVM	LTcP	PLS	82.3	85.3
10	Naïve Bayes			80.1	82.1
10	Softmax			82.2	83.8
10	Decision tree			79.6	85.4
15	SVM	LTcP	PLS	87.8	88.9
15	Naïve Bayes			85.4	87.9
15	Softmax			87.3	85.4
15	Decision tree			84.8	86.6
20	SVM	LTcP	PLS	88.6	91.7
20	Naïve Bayes			88.5	92.6
20	Softmax			89.8	97.8
20	Decision tree			87.5	99.3
25	SVM	LTcP	PLS	89.7	92.4
25	Naïve Bayes			86.7	98.7
25	Softmax			88.4	101.4
25	Decision tree			87.5	109
30	SVM	LTcP	PLS	91.4	92.6
30	Naïve Bayes			90.2	99.7
30	Softmax			87.5	105.4
30	Decision tree			89.4	112.5
35	SVM	LTcp	PLS	91.4	94.4
35	Naïve Bayes			90.2	98.7
35	Softmax			90.3	106.1
35	Decision tree			90.5	120.3

Comparison among approaches based on recall and F1 score on the brats2018 dataset

Approach	Recall	F1 score
SVM	94.60	93.20
Naïve Bayes	89.45	88.80
Softmax	94.45	92.93
Decision Tree	89.34	89.35
Proposed approach (SVM as classifier)	95.90	96.80
Proposed approach (Naïve Bayes as classifier)	94.03	94.5
Proposed approach (Softmax as classifier)	95.10	96.95
Proposed approach (decision tree as classifier)	93.20	94.10

DOI: https://doi.org/10.2478/ijssis-2025-0054 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Jul 8, 2025

Published on: Oct 20, 2025

Published by: Professor Subhas Chandra Mukhopadhyay

In partnership with: Paradigm Publishing Services

Publication frequency: 1 times per year

Keywords:

local tetra patterns based on the composite plane,

partial least squares,

tumors,

superpixel segmentation

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2025 Ravi Prakash Chaturvedi, Annu Mishra, Mohd Dilshad Ansari, Ajay Shriram Kushwaha, Prakhar Mittal, Rajneesh Kumar Singh, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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A new local tetra pattern in composite planes (LTcP) technique for classifying brain tumors using partial least squares and super-pixel segmentation

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Comparison of results based on different classifiers using fused features of image (brats2018)

Numbers of features extracted using the proposed method

Comparison of results based on different classifiers using various numbers of fused features of the image (brats2019)

Comparison among approaches based on recall and F1 score on the brats2018 dataset

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