Computational Intelligence in Magnetic Resonance Imaging of the Human Brain: The Classic-Curvature and the Intensity-Curvature Functional in a Tumor Case Study

Ciulla, Carlo; Bogatinoska, Dijana Capeska; Risteski, Filip A.; Veljanovski, Dimitar

doi:10.5815/ijieeb.2014.02.01

Cited by 2 publications

(8 citation statements)

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“…MRI provides the information related to the anatomy of the pathology and such information is used in order to classify the tumor. The tumor embeds in its structure the key to the correct diagnosis and moreover embeds details that can be enhanced through the post-processing of the MRI images [1] , [2] , [3] .…”

Section: Introductionmentioning

confidence: 99%

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors

Ciulla

Veljanovski²,

Shikoska

et al. 2015

Journal of Advanced Research

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This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional.

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Section: Introductionmentioning

confidence: 99%

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors

Ciulla

Veljanovski²,

Shikoska

et al. 2015

Journal of Advanced Research

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“…In order to frame into the literature the works of this research, which recalls works reported earlier [7,[12][13][14][15][16][17], it is necessary to consider the rationale of the study presented in this paper. So far, no studies have been reported attempting to achieve the characterization of the intensity-curvature term before interpolation.…”

Section: The Scope Of the Researchmentioning

confidence: 99%

“…The classic-curvature images have been object of considerable study [7,12,14,17] and allow to distinguish clearly the brain tumor. Moreover, similarly to the original MRI image, the classic-curvature image shows a clear layout of the tumor and the surrounding brain structures.…”

Section: The Scope Of the Researchmentioning

confidence: 99%

“…This paper is concerned with the calculation of the classic-curvature image [12] and the calculation of the intensity-curvature functional term before interpolation image [13][14][15][16][17] using MRI data consisting of a twodimensional image of a human brain affected by a tumor.…”

Section: A the Literaturementioning

confidence: 99%

“…This paper departs from a bivariate model function fitted to MRI data of the human brain in pathological state (tumor) and the mathematical procedure developed herein consists of two steps [7,[12][13][14][15][16][17]]:…”

Section: B the Theoretical Efforts Of The Researchmentioning

confidence: 99%

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Calculation of the Classic-Curvature and the Intensity-Curvature Term Before Interpolation of a Bivariate Polynomial

Agyapong

2015

IJIEEB

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Abstract-This paper presents the calculation of the classic-curvature and the intensity-curvature term before interpolation of a bivariate polynomial model function. The classic-curvature is termed as y c (x, y) and the intensity-curvature term before interpolation is termed as E 0 . The classic-curvature is defined as the sum of the four second order partial derivatives of the bivariate polynomial. The intensity-curvature term before interpolation is defined as the integral of the product between the pixel intensity value termed as f(0, 0) and the classic-curvature calculated at the origin of the coordinate system of the pixel. This paper presents an application of the calculation of classic-curvature and the intensitycurvature term before interpolation using twodimensional Magnetic Resonance Imaging (MRI) data and reports for the first time in the literature on the behavior of the intensity-curvature term before interpolation.Index Terms-Magnetic Resonance Imaging (MRI), classic-curvature, intensity-curvature term before interpolation, intensity-curvature measurement approaches, first order partial derivative, second order partial derivative, bivariate polynomial, model function, image.

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Computational Intelligence in Magnetic Resonance Imaging of the Human Brain: The Classic-Curvature and the Intensity-Curvature Functional in a Tumor Case Study

Abstract: Abstract-This

Cited by 2 publications

References 12 publications

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors

Calculation of the Classic-Curvature and the Intensity-Curvature Term Before Interpolation of a Bivariate Polynomial

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