Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

910
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
910
Upsampling01:22

Upsampling

743
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
743
Downsampling01:20

Downsampling

864
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
864
Scaling01:26

Scaling

709
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
709
Deconvolution01:20

Deconvolution

763
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
763
Convolution Properties II01:17

Convolution Properties II

750
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
750

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Automatic classification of circulating blood cell clusters based on multi-channel flow cytometry imaging.

Engineering applications of artificial intelligence·2026
Same author

Relational Graph Convolutional Network with BERT Embeddings for Ontology Relationship Classification.

Studies in health technology and informatics·2026
Same author

Experimental Validation and Bioinformatics Analysis Elucidate the Role of MTDH-Mediated PTEN Ubiquitination and Degradation in Podocyte Injury in Diabetic Kidney Disease.

Human mutation·2026
Same author

Gradient-based rigid motion correction in CBCT via Lie algebra-constrained registration.

Physics in medicine and biology·2026
Same author

An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays.

Lab on a chip·2026
Same author

BrainUMA: A Unified multi-atlas learning framework for brain disorders diagnosis.

Medical & biological engineering & computing·2026
Same journal

Experimental study on deantigenization and trabecular structure effects on bovine cancellous bone compression.

Bio-medical materials and engineering·2026
Same journal

Effects of dentin extract without demineralization on migration and angiogenic potential of human umbilical vein endothelial cells.

Bio-medical materials and engineering·2026
Same journal

Measurement of thermal expansion coefficient of melanin for photoacoustic technology.

Bio-medical materials and engineering·2026
Same journal

Development of chitosan-selenium nanoparticle modified brushite cement: A potential strategy for improved clinical performance in bone regeneration.

Bio-medical materials and engineering·2026
Same journal

Electrostatic layer-by-layer assembly for fabricating morphology-controlled hydroxyapatite/zirconia composite with enhanced osteogenic performance.

Bio-medical materials and engineering·2026
Same journal

The antitumor activity of bismuth lipophilic nanoparticles (BisBAL NPs) on human glioblastoma is higher than temozolomide.

Bio-medical materials and engineering·2026
See all related articles

Related Experiment Video

Updated: Apr 23, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.1K

Improved Hessian multiscale enhancement filter.

Jinzhu Yang1, Shuang Ma1, Qi Sun2

  • 1Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, Shenyang 110819, China College of Information Science and Engineering, Northeastern University, Shenyang 110004, China.

Bio-Medical Materials and Engineering
|September 18, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Hessian multiscale filter for medical image analysis. The enhanced filter effectively reduces pseudo-vascular structures and noise, improving cerebrovascular segmentation accuracy.

Keywords:
CT imageHessian filterMRA imagegrayscalemultiscale enhancement

More Related Videos

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

11.5K

Related Experiment Videos

Last Updated: Apr 23, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.1K
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

11.5K

Area of Science:

  • Medical Image Analysis
  • Computer-Aided Diagnosis

Background:

  • Traditional Hessian multiscale filters rely solely on local geometric features, neglecting global grayscale information.
  • In medical imaging, Hessian filters enhance vasculature but generate artifacts like pseudo-vascular structures and noise, complicating segmentation.
  • Nasal soft tissues in MRA data exemplify noise with vessel-like shapes, hindering cerebrovascular segmentation.

Purpose of the Study:

  • To address limitations of traditional Hessian filters in medical image analysis.
  • To develop an improved Hessian multiscale filter incorporating global grayscale information.
  • To enhance the accuracy of cerebrovascular segmentation by reducing artifacts.

Main Methods:

  • An improved Hessian multiscale filter was developed.
  • An image grayscale factor was integrated into the vascular similarity function.
  • The function utilizes Hessian matrix eigenvalues for computation.

Main Results:

  • The improved filter was tested on brain MRA and lung CTA data.
  • Enhanced visualization of vascular structures was achieved.
  • Reduction in pseudo-vascular structures and isolated noise points was observed.

Conclusions:

  • The proposed improved Hessian multiscale filter effectively enhances vascular structures.
  • The method successfully reduces artifact formation, improving segmentation quality.
  • This approach offers a more robust solution for medical image analysis, particularly for cerebrovascular segmentation.