Intensity-curvature highlight of human brain magnetic resonance imaging vasculature

2019

High Frequency

Self Cite

This research examines the use of the intensity‐curvature functional (ICF) as filter in image space and in k‐space. The novelty of this study is three‐folded: (a) The evidence that the ICF calculated from three additional (International Journal of Imaging Systems and Technology, 28, 2018, 54) two‐dimensional model polynomial functions is an image space filter; (b) An additional (The use of the intensity‐curvature functional as k‐space filter: Applications in magnetic resonance imaging of the human brain, 2018) ICF‐based k‐space filtering technique applicable to two‐dimensional magnetic resonance images; (c) Results obtained through the calculation of the ICF of the trivariate cubic Lagrange model polynomial function (LGR3D). Although ICF‐based k‐space filtering delivers clear and well‐defined images, ICF‐based image space filtering remains superior when reconstructing vessel images in T2 MRI. The ICF of the LGR3D function provides sharp images too.

Section: Discussionmentioning

confidence: 90%

Section: Bivariate Cubic Lagrange Model Function (G42d)mentioning

confidence: 86%

Section: Bivariate Cubic Model Function (B32d)mentioning

confidence: 99%

Section: Bivariate Cubic Lagrange Model Function (G42d)mentioning

confidence: 99%

See 2 more Smart Citations

Intensity‐curvature functional‐based filtering in image space and k‐space: Applications in magnetic resonance imaging of the human brain

2019

High Frequency

Self Cite

“…Since its inception, the ICF has been used to study human brain tumors which are imaged with MRI [38] and human brain vasculature which is also imaged with MRI [39], and to mathematically characterize the input and output functions of the ICF-based filters using three different model polynomial functions [1]. The appearance of the ICF image as HPF signal demands an explanation.…”

Section: Contributionmentioning

confidence: 99%

Intensity-curvature functional based digital high pass filter of the bivariate cubic B-spline model polynomial function

Vis. Comput. Ind. Biomed. Art

Agyapong

2019

Self Cite

This research addresses the design of intensity-curvature functional (ICF) based digital high pass filter (HPF). ICF is calculated from bivariate cubic B-spline model polynomial function and is called ICF-based HPF. In order to calculate ICF, the model function needs to be second order differentiable and to have non-null classic-curvature calculated at the origin (0, 0) of the pixel coordinate system. The theoretical basis of this research is called intensitycurvature concept. The concept envisions to replace signal intensity with the product between signal intensity and sum of second order partial derivatives of the model function. Extrapolation of the concept in two-dimensions (2D) makes it possible to calculate the ICF of an image. Theoretical treatise is presented to demonstrate the hypothesis that ICF is HPF signal. Empirical evidence then validates the assumption and also extends the comparison between ICF-based HPF and ten different HPFs among which is traditional HPF and particle swarm optimization (PSO) based HPF. Through comparison of image space and k-space magnitude, results indicate that HPFs behave differently. Traditional HPF filtering and ICF-based filtering are superior to PSO-based filtering. Images filtered with traditional HPF are sharper than images filtered with ICF-based filter. The contribution of this research can be summarized as follows: (1) Math description of the constraints that ICF need to obey to in order to function as HPF; (2) Math of ICF-based HPF of bivariate cubic B-spline; (3) Image space comparisons between HPFs; (4) K-space magnitude comparisons between HPFs. This research provides confirmation on the math procedure to use in order to design 2D HPF from a model bivariate polynomial function.

The use of the intensity‐curvature measurement approaches: Applications in magnetic resonance imaging of the human brain

2019

Engineering Reports

Self Cite

This research describes signal processing techniques useful to highlight and contrast enhance human brain vessels detected with magnetic resonance imaging (MRI). The signal is here represented as multiplication between image intensity and the sum of second‐order partial derivatives of the model function fitted to MRI data. The implication of this novel and different concept is the introduction of the new variable called intensity‐curvature. The following six intensity‐curvature measurement approaches (ICMAs) can hence be calculated: classic‐curvature, intensity‐curvature functional (ICF), signal resilient to interpolation, resilient curvature, and intensity‐curvature terms (ICTs) before and after interpolation. This paper explores the use of ICMAs with applications in human brain MRI. Studying vasculature of the human brain is possible through two techniques apt to measure human brain MRI intensity‐curvature. The first technique is the inverse Fourier transformation procedure, which uses as k‐space filters the ICTs before and after interpolation. The other technique is called intensity‐curvature masking procedure, and it raises each ICMA to a power determining a nonlinear combination of ICMAs to sum up to MRI image intensity on a pixel‐by‐pixel basis. This paper reports that ICTs are k‐space filters, ICF is a high‐pass filter, and ICMAs are mask images. The major implication of these methodologies is effective k‐space filtering. The techniques here presented support visualization of human brain vessels. The advantage of these methods relies in speed of computation.