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

You might also read

Related Articles

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

Sort by
Same author

Comparative evaluation of ADC and ADC ratio in differentiating Gleason Score 7 in prostate cancer imaging.

European journal of radiology open·2025
Same author

Prediction of complicated appendicitis risk in children.

European review for medical and pharmacological sciences·2021
Same author

Behavioural and emotional profile of children in residential care in Greece.

Psychiatrike = Psychiatriki·2020
Same author

Imaging biomarker analysis of advanced multiparametric MRI for glioma grading.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2019
Same author

Contrast-enhanced and unenhanced diffusion-weighted imaging of the breast at 3 T.

Clinical radiology·2018
Same author

Reproducibility of apparent diffusion coefficient measurements evaluated with different workstations.

Clinical radiology·2017

Related Experiment Video

Updated: Apr 1, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.9K

A two-stage method for microcalcification cluster segmentation in mammography by deformable models.

N Arikidis1, K Vassiou2, A Kazantzi1

  • 1Department of Medical Physics, School of Medicine, University of Patras, Patras 26504, Greece.

Medical Physics
|October 3, 2015
PubMed
Summary
This summary is machine-generated.

This study presents a reliable semiautomated method for segmenting microcalcification clusters in mammography, improving accuracy for computer-aided diagnosis. The new approach significantly outperforms existing methods, aiding radiologists in quantitative image analysis.

More Related Videos

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

15.2K
Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

710

Related Experiment Videos

Last Updated: Apr 1, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.9K
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

15.2K
Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

710

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Image Segmentation

Background:

  • Accurate segmentation of microcalcification (MC) clusters in mammography is crucial for quantitative analysis and computer-aided diagnosis.
  • Current segmentation methods face challenges in precision and efficiency for radiologists.

Purpose of the Study:

  • To investigate a two-stage semiautomated segmentation method for microcalcification (MC) clusters in mammography.
  • To evaluate the reliability and accuracy of the proposed segmentation method compared to existing techniques.

Main Methods:

  • A two-stage semiautomated approach combining level set and active contour models within a wavelet transform scale-space.
  • Evaluation of segmentation reliability using inter/intraobserver agreements and quantitative metrics (Hausdorff distance, average minimum distance, area overlap measure).
  • Assessment of the method's impact on MC cluster characterization accuracy using feature extraction and support vector machine classification.

Main Results:

  • Substantial interobserver and intraobserver agreements were achieved for distance-based metrics, with moderate agreement for area overlap.
  • The proposed semiautomated method demonstrated statistically significant superior performance (Az=0.80 ± 0.04) compared to the B-spline active rays method (Az=0.69 ± 0.04).
  • The method shows promise for improving MC cluster characterization accuracy in computer-aided diagnosis.

Conclusions:

  • Deformable models offer a reliable semiautomated segmentation method for microcalcification (MC) clusters.
  • The proposed method can be effectively utilized for quantitative image analysis of MC clusters in mammography.