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 Videos

Tissue classification with generalized spectrum parameters.

K D Donohue1, L Huang, T Burks

  • 1Electrical and Computer Engineering Department, University of Kentucky, Lexington, KY 40506-0046, USA. donohue@engr.uky.edu

Ultrasound in Medicine & Biology
|December 26, 2001
PubMed
Summary
This summary is machine-generated.

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

Clinical impact of CEUS on non-characterizable observations and observations with intermediate probability of malignancy on CT/MRI in patients at risk for HCC.

Abdominal radiology (New York)·2024
Same author

Effects of acute lying and sleep deprivation on metabolic and inflammatory responses of lactating dairy cows.

Journal of dairy science·2021
Same author

Transrectal Subharmonic Ultrasound Imaging for Prostate Cancer Detection.

Urology·2020
Same author

Development of a noninvasive system for monitoring dairy cattle sleep.

Journal of dairy science·2016
Same author

Increased fragmentation of sleep-wake cycles in the 5XFAD mouse model of Alzheimer's disease.

Neuroscience·2015
Same author

Subharmonic aided pressure estimation for monitoring interstitial fluid pressure in tumours--in vitro and in vivo proof of concept.

Ultrasonics·2014
Same journal

Cardiac Natural Mechanical Wave Detection and Speed Estimation Using Deep Learning-Based 2-D Ultrasound Imaging: A Feasibility Study.

Ultrasound in medicine & biology·2026
Same journal

Region-Specific Evaluation of Plaque Segmentation in Cross-sectional Projections of Carotid Ultrasound Images Using Deep Learning Models in a Sub-clinical Atherosclerosis Cohort.

Ultrasound in medicine & biology·2026
Same journal

Simulating the Dedifferentiation Process of Thyroid Cancer: Insights from Mouse Models and Ultrasound Imaging.

Ultrasound in medicine & biology·2026
Same journal

A Nomogram Based on Ultrasound Features for Predicting Major Intra-Operative Hemorrhage in Patients With Placenta Accreta Spectrum (PAS).

Ultrasound in medicine & biology·2026
Same journal

MedLP-HAFB-CLIP: Hierarchical Adaptive Large Model With Learnable Medical Prompts for Level II Ultrasound Standard Plane Identification.

Ultrasound in medicine & biology·2026
Same journal

Data Assimilating B-splines for Model-based Regularization in Ultrasound Vector Flow Imaging.

Ultrasound in medicine & biology·2026
See all related articles

Generalized spectrum (GS) analysis significantly improves breast tumor classification accuracy compared to conventional texture analysis (CTA). GS-based classifiers show over 10% improvement in receiver operating characteristic (ROC) areas for detecting malignant breast masses.

Area of Science:

  • Medical Imaging
  • Biophysics
  • Signal Processing

Background:

  • Accurate breast tumor classification is crucial for effective diagnosis and treatment.
  • Conventional texture analysis (CTA) has limitations in differentiating benign from malignant breast masses.
  • Radiofrequency (RF) ultrasonic scans contain rich information about tissue scatterer properties.

Purpose of the Study:

  • To compare the performance of breast tumor classifiers using conventional texture analysis (CTA) and generalized spectrum (GS) parameters.
  • To evaluate the relationship between GS-based parameters and underlying scatterer properties in breast tumors.
  • To assess the clinical efficacy of GS-based classifiers in differentiating benign and malignant breast masses.

Main Methods:

  • Radiofrequency (RF) ultrasonic scans from 40 patients (22 benign, 24 malignant breast masses) were analyzed.

Related Experiment Videos

  • Generalized spectrum (GS) parameters were computed and related to scatterer properties.
  • Linear classifiers were developed using CTA and GS parameters from tumor front edge, back edge, and interior regions.
  • Classifier performance was evaluated using receiver operating characteristic (ROC) analysis.

    • Main Results:

    • GS-based classifiers demonstrated significantly superior performance compared to CTA-based classifiers.
    • Improvements in empirical receiver operating characteristic (ROC) areas exceeded 10% for GS-based classifiers.
    • GS-based classifiers achieved high sensitivity and specificity, e.g., 90% sensitivity at 50% specificity for back-edge regions.

    Conclusions:

    • Generalized spectrum (GS) analysis offers a more effective approach for breast tumor classification than conventional texture analysis (CTA).
    • GS-based parameters derived from RF ultrasonic scans provide valuable insights into tissue characteristics for improved diagnostic accuracy.
    • GS-based classifiers show promise for enhanced non-invasive detection of malignant breast tumors.