Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Auto-shape lossless compression of pharynx and esophagus fluoroscopic images.

Journal of medical systems·2015

Same author

Prediction models for early risk detection of cardiovascular event.

Journal of medical systems·2012

Same author

Improved biliary detection and diagnosis through intelligent machine analysis.

Computer methods and programs in biomedicine·2011

Same author

Magnetic resonance cholangiopancreatography image enhancement for automatic disease detection.

World journal of radiology·2010

Same author

Using autoencoders for mammogram compression.

Journal of medical systems·2010

Same author

A novel strategy for load balancing of distributed medical applications.

Journal of medical systems·2010

Search research articles

Related Experiment Video

Updated: Jul 18, 2026

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Published on: March 7, 2017

Stone detection in MRCP images using controlled region growing.

Rajasvaran Logeswaran¹, Chikkannan Eswaran

¹Faculty of Engineering, Multimedia University, 63100 Cyberjaya, Malaysia. loges@mmu.edu.my

Computers in Biology and Medicine

|November 23, 2006

Summary

This study introduces a new semi-automated method for detecting gallstones in magnetic resonance cholangiopancreatography (MRCP) images. The technique achieves over 90% accuracy, aiding in the diagnosis of biliary tract stones.

More Related Videos

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

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

Published on: June 24, 2025

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

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

Published on: August 30, 2013

Related Experiment Videos

Last Updated: Jul 18, 2026

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Published on: March 7, 2017

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

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

Published on: June 24, 2025

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

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

Published on: August 30, 2013

Area of Science:

Medical Imaging
Diagnostic Radiology
Computational Pathology

Background:

Biliary tract stones are commonly diagnosed using MRCP.
Automatic detection of stones in MRCP images is challenging due to image noise and stone variability.
Existing algorithms struggle with varying stone intensity, size, and location.

Purpose of the Study:

To develop a semi-automated, segment-based scheme for detecting choledocholithiasis and cholelithiasis in MRCP images.
To improve the accuracy and reliability of computer-aided diagnosis for biliary stones.
To differentiate between normal and abnormal MRCP images indicative of stones.

Main Methods:

A multi-stage, segment-based approach was proposed for stone detection.
The scheme was tested on MRCP images to evaluate its performance.
The method focused on semi-automated detection to overcome limitations of fully automated algorithms.

Main Results:

The proposed scheme demonstrated good performance in tests.
The system successfully differentiated between MRCP images with and without stones.
A high success rate exceeding 90% was achieved in detecting biliary stones.

Conclusions:

The developed scheme shows significant potential for clinical application in diagnosing biliary tract stones.
Refinement of this method could lead to a valuable tool for clinicians.
Potential applications include aiding diagnosis and supporting telemedicine initiatives.