A Novel Approach to T2-Weighted MRI Filtering: The Classic-Curvature and the Signal Resilient to Interpolation Filter Masks

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Author(s)

Carlo Ciulla 1,* Farouk Yahaya 1 Edmund Adomako 1 Ustijana Rechkoska Shikoska 1 Grace Agyapong 1 Dimitar Veljanovski 2 Filip A. Risteski 2

1. University of Information Science & Technology, ―St. Paul the Apostle‖, Partizanska B.B., 6000, Ohrid, Macedonia

2. General Hospital 8-mi Septemvri, Department of Radiology, Boulevard 8th September, 1000, Skopje, Macedonia

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2016.01.01

Received: 25 Sep. 2015 / Revised: 5 Nov. 2015 / Accepted: 2 Dec. 2015 / Published: 8 Jan. 2016

Index Terms

Magnetic Resonance Imaging, Human Brain Tumor, Classic-Curvature, CC, Signal Resilient to Interpolation, SRI, Filter Mask, Convolution

Abstract

This paper presents a novel and unreported approach developed to filter T2-weighetd Magnetic Resonance Imaging (MRI). The MRI data is fitted with a parametric bivariate cubic Lagrange polynomial, which is used as the model function to build the continuum into the discrete samples of the two-dimensional MRI images. On the basis of the aforementioned model function, the Classic-Curvature (CC) and the Signal Resilient to Interpolation (SRI) images are calculated and they are used as filter masks to convolve the two-dimensional MRI images of the pathological human brain. The pathologies are human brain tumors. The result of the convolution provides with filtered T2-weighted MRI images. It is found that filtering with the CC and the SRI provides with reliable and faithful reproduction of the human brain tumors. The validity of filtering the T2-weighted MRI for the quest of supplemental information about the tumors is also found positive.

Cite This Paper

Carlo Ciulla, Farouk Yahaya, Edmund Adomako, Ustijana Rechkoska Shikoska, Grace Agyapong, Dimitar Veljanovski, Filip A. Risteski, "A Novel Approach to T2-Weighted MRI Filtering: The Classic-Curvature and the Signal Resilient to Interpolation Filter Masks", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.1, pp.1-10, 2016. DOI:10.5815/ijieeb.2016.01.01

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