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Tumor detection in MRI brain images based on saliency computational modeling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Tumor detection in MRI brain images based on saliency computational modeling. / Jian, Muwei; Zhang, Xianxin; Ma, Lifu; Yu, Hui.

The 3rd IFAC Conference on Cyber-Physical & Human-Systems. Institute of Electrical and Electronics Engineers, 2020.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Jian, M, Zhang, X, Ma, L & Yu, H 2020, Tumor detection in MRI brain images based on saliency computational modeling. in The 3rd IFAC Conference on Cyber-Physical & Human-Systems. Institute of Electrical and Electronics Engineers, The 3rd IFAC Workshop on Cyber-Physical & Human Systems, Beijing, China, 3/12/20. <https://www.cphs2020.org/>

APA

Jian, M., Zhang, X., Ma, L., & Yu, H. (Accepted/In press). Tumor detection in MRI brain images based on saliency computational modeling. In The 3rd IFAC Conference on Cyber-Physical & Human-Systems Institute of Electrical and Electronics Engineers. https://www.cphs2020.org/

Vancouver

Jian M, Zhang X, Ma L, Yu H. Tumor detection in MRI brain images based on saliency computational modeling. In The 3rd IFAC Conference on Cyber-Physical & Human-Systems. Institute of Electrical and Electronics Engineers. 2020

Author

Jian, Muwei ; Zhang, Xianxin ; Ma, Lifu ; Yu, Hui. / Tumor detection in MRI brain images based on saliency computational modeling. The 3rd IFAC Conference on Cyber-Physical & Human-Systems. Institute of Electrical and Electronics Engineers, 2020.

Bibtex

@inproceedings{8e5df7ac43364f00b70d246b863b3df5,
title = "Tumor detection in MRI brain images based on saliency computational modeling",
abstract = "In recent years, the issue of tumor detection for Magnetic Resonance Imaging (MRI) brain images has become a research hotspot in the field of medical imaging, multimedia and pattern recognition. In this paper, we propose a tumor detection method based on saliency modeling for MRI brain images. Firstly, in order to overcome the influence of the skull, we utilize the morphological method to strip the skull of the MRI brain images. Then, we introduce a principal local contrast based saliency-detection method to enhance the foreground regions which facilitates to get the leision region. Finally, the results are further improved by denoising, segmentation and morphological operations. Experiments performed on MRI brain images show that the proposed method is useful and effective.",
keywords = "zeroembargo, brain image, lesion region, saliency detection, morphology, saliency modeling",
author = "Muwei Jian and Xianxin Zhang and Lifu Ma and Hui Yu",
note = "No embargo. {\textcopyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Table misidentified in AAM, but all data is present.; The 3rd IFAC Workshop on Cyber-Physical &amp; Human Systems ; Conference date: 03-12-2020 Through 05-12-2020",
year = "2020",
month = oct,
day = "6",
language = "English",
booktitle = "The 3rd IFAC Conference on Cyber-Physical & Human-Systems",
publisher = "Institute of Electrical and Electronics Engineers",

}

RIS

TY - GEN

T1 - Tumor detection in MRI brain images based on saliency computational modeling

AU - Jian, Muwei

AU - Zhang, Xianxin

AU - Ma, Lifu

AU - Yu, Hui

N1 - No embargo. © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Table misidentified in AAM, but all data is present.

PY - 2020/10/6

Y1 - 2020/10/6

N2 - In recent years, the issue of tumor detection for Magnetic Resonance Imaging (MRI) brain images has become a research hotspot in the field of medical imaging, multimedia and pattern recognition. In this paper, we propose a tumor detection method based on saliency modeling for MRI brain images. Firstly, in order to overcome the influence of the skull, we utilize the morphological method to strip the skull of the MRI brain images. Then, we introduce a principal local contrast based saliency-detection method to enhance the foreground regions which facilitates to get the leision region. Finally, the results are further improved by denoising, segmentation and morphological operations. Experiments performed on MRI brain images show that the proposed method is useful and effective.

AB - In recent years, the issue of tumor detection for Magnetic Resonance Imaging (MRI) brain images has become a research hotspot in the field of medical imaging, multimedia and pattern recognition. In this paper, we propose a tumor detection method based on saliency modeling for MRI brain images. Firstly, in order to overcome the influence of the skull, we utilize the morphological method to strip the skull of the MRI brain images. Then, we introduce a principal local contrast based saliency-detection method to enhance the foreground regions which facilitates to get the leision region. Finally, the results are further improved by denoising, segmentation and morphological operations. Experiments performed on MRI brain images show that the proposed method is useful and effective.

KW - zeroembargo

KW - brain image

KW - lesion region

KW - saliency detection

KW - morphology

KW - saliency modeling

M3 - Conference contribution

BT - The 3rd IFAC Conference on Cyber-Physical & Human-Systems

PB - Institute of Electrical and Electronics Engineers

T2 - The 3rd IFAC Workshop on Cyber-Physical &amp; Human Systems

Y2 - 3 December 2020 through 5 December 2020

ER -

ID: 23212166