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Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors

Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors Abstract.We investigate a new preprocessing approach for MRI glioblastoma brain tumors. Based on combined denoising technique (bilateral filter) and contrast-enhancement technique (automatic contrast stretching based on image statistical information), the proposed approach offers competitive results while preserving the tumor region’s edges and original image’s brightness. In order to evaluate the proposed approach’s performance, quantitative evaluation has been realized through the Multimodal Brain Tumor Segmentation (BraTS 2015) dataset. A comparative study between the proposed method and four state-of-the art preprocessing algorithm attests that the proposed approach could yield a competitive performance for magnetic resonance brain glioblastomas tumor preprocessing. In fact, the result of this step of image preprocessing is very crucial for the efficiency of the remaining brain image processing steps: i.e., segmentation, classification, and reconstruction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors

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Publisher
SPIE
Copyright
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.JMI.6.4.044002
Publisher site
See Article on Publisher Site

Abstract

Abstract.We investigate a new preprocessing approach for MRI glioblastoma brain tumors. Based on combined denoising technique (bilateral filter) and contrast-enhancement technique (automatic contrast stretching based on image statistical information), the proposed approach offers competitive results while preserving the tumor region’s edges and original image’s brightness. In order to evaluate the proposed approach’s performance, quantitative evaluation has been realized through the Multimodal Brain Tumor Segmentation (BraTS 2015) dataset. A comparative study between the proposed method and four state-of-the art preprocessing algorithm attests that the proposed approach could yield a competitive performance for magnetic resonance brain glioblastomas tumor preprocessing. In fact, the result of this step of image preprocessing is very crucial for the efficiency of the remaining brain image processing steps: i.e., segmentation, classification, and reconstruction.

Journal

Journal of Medical Imaging – SPIE

Published: Oct 1, 2019

Keywords: magnetic resonance imaging; preprocessing; contrast stretching; glioblastomas

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