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 Experiment Video

Updated: May 1, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.8K

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector

Maxine Tan1, Jiantao Pu2, Bin Zheng3,2

  • 1School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA. Maxine.Y.Tan-1@ou.edu.

International Journal of Computer Assisted Radiology and Surgery
|March 26, 2014
PubMed
Summary

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

Regulatory crosstalk between KLF5, miR-29a and Fbw7/CDC4 cooperatively promotes atherosclerotic development.

Biochimica et biophysica acta. Molecular basis of disease·2017
Same author

Near-Infrared Light Triggered Upconversion Optogenetic Nanosystem for Cancer Therapy.

ACS nano·2017
Same author

Non-contact method to freely control the radiation patterns of antenna with multi-folded transformation optics.

Scientific reports·2017
Same author

Cost Effectiveness of Imatinib, Dasatinib, and Nilotinib as First-Line Treatment for Chronic-Phase Chronic Myeloid Leukemia in China.

Clinical drug investigation·2017
Same author

Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis: An Assessment.

Academic radiology·2017
Same author

Preliminary study on ultrasound-guided prostate biopsy specimen scores.

Experimental and therapeutic medicine·2017
Same journal

Strain alignment: toward assessing mechanical plausibility of predicted displacement fields.

International journal of computer assisted radiology and surgery·2026
Same journal

Vascular geometry characterization for AI-based endovascular navigation.

International journal of computer assisted radiology and surgery·2026
Same journal

Nail It! A learning framework for autonomous surgical suturing and teleoperation on the dVRK.

International journal of computer assisted radiology and surgery·2026
Same journal

Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.

International journal of computer assisted radiology and surgery·2026
Same journal

BronchoLumen: analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy.

International journal of computer assisted radiology and surgery·2026
Same journal

Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study).

International journal of computer assisted radiology and surgery·2026
See all related articles
This summary is machine-generated.

This study introduces a new feature selection method to improve computer-aided breast mass classification. The method effectively identifies key features like mass shape and fat presence, enhancing diagnostic accuracy for radiologists.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Machine Learning in Healthcare

Background:

  • Improving breast cancer detection and reducing false positives are critical in radiology.
  • Computer-aided diagnosis (CAD) schemes are increasingly researched for breast lesion classification.
  • Accurate feature selection is essential for effective CAD systems.

Purpose of the Study:

  • To investigate a novel feature selection method for classifying malignant and benign breast lesions.
  • To enhance the performance of computer-aided diagnosis (CAD) systems for breast mass classification.
  • To identify image features most relevant for differentiating breast masses.

Main Methods:

  • Computed 181 image features (shape, texture, etc.) from mammograms.
Keywords:
Breast cancerComputer-aided diagnosis of mammogramsFeature selectionPattern classification

Related Experiment Videos

Last Updated: May 1, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.8K
  • Employed sequential forward floating selection (SFFS) for feature selection.
  • Utilized a support vector machine (SVM) model with tenfold cross-validation on 1200 mass regions.

    • Main Results:

    • The SFFS method identified features related to mass shape, isodensity, and fat presence as most significant.
    • These selected features align with those used by radiologists in clinical practice.
    • Mass spiculation features proved difficult to compute accurately from projection mammograms, impacting classification performance.

    Conclusions:

    • The study provides valuable insights for optimizing computerized mass classification schemes.
    • The developed feature selection method shows potential for improving CAD systems.
    • These optimized CAD systems could serve as a "second reader" to assist radiologists in clinical practice.