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: Jun 26, 2026

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
10:33

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

Correlative feature analysis on FFDM.

Yading Yuan1, Maryellen L Giger, Hui Li

  • 1Department of Radiology, Committee on Medical Physics, The University of Chicago, Chicago, Illinois 60637, USA. yading@uchicago.edu

Medical Physics
|January 30, 2009
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

You might also read

Related Articles

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

Sort by
Same author

Ultrafast Bilateral DCE-MRI of the Breast with Conventional Fourier Sampling: Preliminary Evaluation of Semi-quantitative Analysis.

Academic radiology·2016
Same author

Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.

Journal of digital imaging·2012
Same author

A dual-stage method for lesion segmentation on digital mammograms.

Medical physics·2007
Same author

[Characteristics, evolution and variation of M genes of human avian H5N1 strains in Guangdong].

Bing du xue bao = Chinese journal of virology·2007
Same author

Dynamic changes in microbial activity and community structure during biodegradation of petroleum compounds: a laboratory experiment.

Journal of environmental sciences (China)·2007
Same author

Differences in optical transport properties between human meridian and non-meridian.

The American journal of Chinese medicine·2007

This study developed a computer framework to match mammogram lesion images from different views. The system accurately identifies corresponding lesion images, improving diagnostic tools.

Area of Science:

  • Medical Imaging
  • Radiology
  • Computer-Aided Diagnosis

Background:

  • Matching lesion images across mammogram views is crucial for diagnosis.
  • Breast nonrigidity and mammogram projection complicate lesion correspondence.

Purpose of the Study:

  • To develop a computerized framework for differentiating corresponding lesion images from noncorresponding ones.
  • To enhance diagnostic accuracy in mammography using automated image analysis.

Main Methods:

  • A dual-stage segmentation (RGI and active contour) extracts mass lesions.
  • Lesion features (density, size, texture, neighborhood, distance to nipple) are automatically extracted.
  • A two-step Bayesian artificial neural network (BANN) scheme calculates correspondence probability.

Related Experiment Videos

Last Updated: Jun 26, 2026

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
10:33

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

Main Results:

  • The distance feature achieved an AUC of 0.81 +/- 0.02.
  • A feature subset (distance, gradient texture, ROI correlation) yielded an AUC of 0.87 +/- 0.02.
  • Multi-feature analysis significantly outperformed single features.

Conclusions:

  • The proposed framework effectively distinguishes corresponding mammogram lesion pairs.
  • Automated feature extraction and BANNs improve lesion matching accuracy.
  • This approach has potential to enhance both radiologist and CAD system performance.