Utilization of image interpolation and fusion in brain tumor segmentation

Noha A El-Hag; Ahmed Sedik; Ghada M El-Banby; Walid El-Shafai; Ashraf A M Khalaf; Waleed Al-Nuaimy; Fathi E Abd El-Samie; Heba M El-Hoseny

doi:10.1002/cnm.3449

Utilization of image interpolation and fusion in brain tumor segmentation

Int J Numer Method Biomed Eng. 2021 Aug;37(8):e3449. doi: 10.1002/cnm.3449. Epub 2021 Jun 21.

Authors

Noha A El-Hag¹, Ahmed Sedik², Ghada M El-Banby³, Walid El-Shafai^{4

5}, Ashraf A M Khalaf¹, Waleed Al-Nuaimy⁶, Fathi E Abd El-Samie^{4

7}, Heba M El-Hoseny⁸

Affiliations

¹ Department of Electronics and Electrical Communications, Faculty of Engineering, Minia University, Minya, Egypt.
² Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligent, Kafr Elsheikh University, Kafr el-Sheikh, Egypt.
³ Department Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
⁴ Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt.
⁵ Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, Saudi Arabia.
⁶ Department of Electrical and Electronic Engineering, University of Liverpool, Liverpool, UK.
⁷ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
⁸ Department of Electronics and Electrical Communication Engineering, Al-Obour High Institute for Engineering and Technology, Al Obour, Egypt.

PMID: 33599091
DOI: 10.1002/cnm.3449

Abstract

Brain tumor is a mass of anomalous cells in the brain. Medical imagining techniques have a vital role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques are the most popular techniques to localize the tumor area. Brain tumor segmentation is very important for the diagnosis of tumors. In this paper, we introduce a framework to perform brain tumor segmentation, and then localize the region of the tumor, accurately. The proposed framework begins with the fusion of MR and CT images by the Non-Sub-Sampled Shearlet Transform (NSST) with the aid of the Modified Central Force Optimization (MCFO) to get the optimum fusion result from the quality metrics perspective. After that, image interpolation is applied to obtain a High-Resolution (HR) image from the Low-Resolution (LR) ones. The objective of the interpolation process is to enrich the details of the fusion result prior to segmentation. Finally, the threshold and the watershed segmentation are applied sequentially to localize the tumor region, clearly. The proposed framework enhances the efficiency of segmentation to help the specialists diagnose brain tumors.

Keywords: MCFO; brain tumor; image fusion; image interpolation; segmentation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Brain / diagnostic imaging
Brain Neoplasms* / diagnostic imaging
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Tomography, X-Ray Computed