On the filtering properties of classic-curvature and intensity-curvature functional images: applications in magnetic resonance imaging

Ciulla, Carlo; Veljanovski, Dimitar; Risteski, Filip A.; Shikoska, Ustijana Rechkoska

doi:10.1504/ijapr.2016.076989

Cited by 2 publications

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“…2). Recent research reports on the meaning and the nature of the ICMAs showing that: (i) the CC and the ICF are filter masks (Ciulla et al, 2016c), (ii) the signal resilient to interpolation is a filter (Ciulla et al, 2016b), and (iii) the resilient curvature inverts, smooths and magnifies (all at once) the grayscale of the image (Ciulla et al, 2016b). Samples of the SRI and the RC of MRI zones of interest are presented in figures 5 and 6, and the MRIs are presented in figures 3 and 4.…”

Section: Discussionmentioning

confidence: 99%

“…3) The resilient curvature presents the properties of inverting, smoothing and magnifying (in one pass) the grayscale of the MRI (Ciulla et al, 2016b). Figure 7 clarifies the neat difference existing between the high pass filtered MRI image and the E o (x, y) and the E IN (x, y).…”

Section: The K-space Magnitude Of the Classic-curvature Images (Calcumentioning

confidence: 92%

“…2) The signal resilient to interpolation is not a duplication of the original MRI because it behaves as a narrow filter (Ciulla et al, 2016b).…”

Section: The K-space Magnitude Of the Classic-curvature Images (Calcumentioning

confidence: 99%

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The Properties of The Intensity-Curvature Measurement Approaches: Applications In Magnetic Resonance Imaging of The Human Brain

Ciulla

Shikoska

Veljanovski³

et al. 2018

J. Inst. Sci. Tech.

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The intensity-curvature measurement approaches (ICMAs) are re-sampling techniques. The intensity-curvature term is the product between the signal intensity and the classic-curvature and is the root concept at the foundation of the ICMAs. The concept offers six ICMAs: the classic-curvature (CC), the intensity-curvature functional (ICF), the signal resilient to interpolation (SRI), the resilient curvature (RC), the intensity-curvature term before interpolation (Eo(x, y)), and the intensity-curvature term after interpolation (EIN(x, y)). The ICMAs have the following properties. The CC and the ICF are mask images. The SRI is a filter. The RC is adept to invert, and simultaneously, to smooth and to magnify the grayscale of the image. The aforementioned properties are illustrated with two-dimensional theoretical images and with Magnetic Resonance Imaging (MRI) images of the human brain. The novelty of this work consists of the use of the Eo(x, y) and the EIN(x, y) in order to highlight human brain vessels identified with MRI. Journal of Institute of Science and Technology Volume 22, Issue 2, January 2018, Page: 19-31

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Section: Discussionmentioning

confidence: 99%

Section: The K-space Magnitude Of the Classic-curvature Images (Calcumentioning

confidence: 92%

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The Properties of The Intensity-Curvature Measurement Approaches: Applications In Magnetic Resonance Imaging of The Human Brain

Ciulla

Shikoska

Veljanovski³

et al. 2018

J. Inst. Sci. Tech.

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“…59 CC and ICF provided a new domain with details about the human brain tumor. 59 The properties of ICF of MRI images were further explored presenting new methodology apt to create high-pass filters from model polynomial functions. 11,12,60 The function needs to hold the property of second-order differentiability.…”

Section: Related Researchmentioning

confidence: 99%

“…Detailed description of the mathematic developments that calculate ICMAs when fitting model functions to image data were reported earlier. 47,48,59 ICMAs were CC, ICF, SRI,…”

Section: Specifications Samples Datasets Icmas Subjects and Mri Acqui...mentioning

confidence: 99%

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

Ciulla

2019

Engineering Reports

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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.

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On the filtering properties of classic-curvature and intensity-curvature functional images: applications in magnetic resonance imaging

Cited by 2 publications

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The Properties of The Intensity-Curvature Measurement Approaches: Applications In Magnetic Resonance Imaging of The Human Brain

The Properties of The Intensity-Curvature Measurement Approaches: Applications In Magnetic Resonance Imaging of The Human Brain

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

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