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

Variation in mental health-related sickness absence duration: The role of occupational health professionals.

PloS one·2026
Same author

Development and internal validation of mammography feature-based prognostic models for distant recurrence-free survival of invasive breast cancer in a screening cohort.

NPJ breast cancer·2026
Same author

Mammo-AGE: deep learning estimation of breast age from mammograms.

Nature communications·2025
Same author

Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history.

NPJ breast cancer·2025
Same author

AI as an independent second reader in detection of clinically relevant breast cancers within a population-based screening programme in the Netherlands: a retrospective cohort study.

The Lancet. Digital health·2025
Same author

Breast cancer in women after withdrawing from an increased-risk MRI screening program.

European radiology·2025

Related Experiment Video

Updated: Dec 30, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

2.4K

A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

Sheila Timp1, Nico Karssemeijer

  • 1Department of Radiology, University Medical Center Nijmegen, The Netherlands. s.timp@rad.umcn.nl

Medical Physics
|June 12, 2004
PubMed
Summary
This summary is machine-generated.

A novel dynamic programming method significantly improves mass segmentation in computer-aided diagnosis (CAD) systems, enhancing lesion classification accuracy for better cancer detection.

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.0K
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

43.5K

Related Experiment Videos

Last Updated: Dec 30, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

2.4K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.0K
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

43.5K

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Image Segmentation

Background:

  • Accurate mass segmentation is vital for computer-aided diagnosis (CAD) systems.
  • Classifying lesions as normal, benign, or malignant relies on precise segmentation.

Purpose of the Study:

  • To present a robust, automated dynamic programming technique for mass lesion segmentation.
  • To ensure closed contours in segmentation results.
  • To evaluate the impact of segmentation on CAD system performance.

Main Methods:

  • A dynamic programming approach for segmenting mass lesions.
  • An algorithm to ensure closed contours.
  • Quantitative comparison with region growing and discrete contour model methods using an overlap criterion on 1210 masses.
  • Evaluation of CAD system detection and classification performance.

Main Results:

  • Dynamic programming achieved a mean overlap percentage of 0.69, significantly outperforming other methods (0.60, 0.59).
  • Detection performance was similar across all segmentation methods.
  • Dynamic programming yielded a statistically significant higher area under the ROC curve (Az=0.74) for classifying lesion malignancy compared to region growing (Az=0.67).

Conclusions:

  • Dynamic programming offers a superior method for mass segmentation in CAD systems.
  • Improved segmentation enhances the classification accuracy of malignant versus benign lesions.
  • This technique holds promise for advancing diagnostic capabilities in medical imaging.