research-article

A brain tumor detection system using gradient based watershed marked active contours and curvelet transform

Author:

Pelin GörgelAuthors Info & Claims

Transactions on Emerging Telecommunications Technologies, Volume 32, Issue 9

https://doi.org/10.1002/ett.4170

Published: 08 September 2021 Publication History

Abstract

Computer aided brain tumor detection is an efficient research area in brain image processing. In this study, a methodology called GWMAC‐CT (gradient based watershed marked active contours and curvelet transform) is proposed to detect the brain tumors in magnetic resonance (MR) images. The implemented system is based on skull removing, segmentation of region of interest (ROI), feature extraction, and ROI classification as tumor or nontumor. The proposed GWMAC is a two‐stage segmentation method which includes gradient based watershed transform (GWT) and improved active contours. The rough ROIs obtained with GWT are utilized as initial contours for the improved active contours method instead of marking initial contours manually. Curvelet transform‐based features of the exact ROI contours are classified via well‐known classification methods such as support vector machine (SVM), K‐nearest neighbors, random forest tree, and Naïve Bayes. Experiments are carried out on a set of brain MR images from BRATS database to demonstrate the effectiveness of the proposed method. The performance evaluators such as accuracy, kappa statistics, false positive rate, precision, F1‐measure, and area under ROC curve are calculated as 96.81%, 0.927, 0.046, 0.905, 0.95, and 0.977, respectively with SVM.

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Cited By

Kurian SJuliet S(2023)An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation modelSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07457-227:18(13305-13319)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s00500-022-07457-2
Kalam RThomas CRahiman M(2022)Brain tumor detection in MRI images using Adaptive-ANFIS classifier with segmentation of tumor and edemaSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07687-427:5(2279-2297)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s00500-022-07687-4

Index Terms

A brain tumor detection system using gradient based watershed marked active contours and curvelet transform
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Transactions on Emerging Telecommunications Technologies

Transactions on Emerging Telecommunications Technologies Volume 32, Issue 9

September 2021

481 pages

ISSN:2161-3915

EISSN:2161-3915

DOI:10.1002/ett.v32.9

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© 2020 John Wiley & Sons, Ltd.

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John Wiley & Sons, Inc.

United States

Publication History

Published: 08 September 2021

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Cited By

Kurian SJuliet S(2023)An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation modelSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07457-227:18(13305-13319)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s00500-022-07457-2
Kalam RThomas CRahiman M(2022)Brain tumor detection in MRI images using Adaptive-ANFIS classifier with segmentation of tumor and edemaSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07687-427:5(2279-2297)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s00500-022-07687-4

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