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

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier.

Biomedical physics & engineering express·2024

Same author

CovTANet: A Hybrid Tri-Level Attention-Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans.

IEEE transactions on industrial informatics·2023

Same author

CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans.

IEEE transactions on artificial intelligence·2023

Same author

BAWGNet: Boundary aware wavelet guided network for the nuclei segmentation in histopathology images.

Computers in biology and medicine·2023

Same author

MD-CardioNet: A Multi-Dimensional Deep Neural Network for Cardiovascular Disease Diagnosis from Electrocardiogram.

IEEE journal of biomedical and health informatics·2023

Same author

SR-AttNet: An Interpretable Stretch-Relax Attention based Deep Neural Network for Polyp Segmentation in Colonoscopy Images.

Computers in biology and medicine·2023

Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026

Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026

Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026

Same journal

An AI-driven deep learning pipeline for taxonomic classification and biodiversity assessment of deep-sea environmental DNA.

Computers in biology and medicine·2026

Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026

Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 4, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic

Amit Kumar Kundu¹, Shaikh Anowarul Fattah¹

¹Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Bangladesh.

Computers in Biology and Medicine

|November 8, 2019

Summary

This summary is machine-generated.

This study introduces an improved method for detecting gastrointestinal bleeding in wireless capsule endoscopy (WCE) images by fitting a Rayleigh probability density function (PDF) to local spatial features, enhancing accuracy and reducing complexity.

Keywords:

Bleeding detection Bleeding zone localization Pixels of interest Rayleigh probability density function (PDF)Support vector machine Wireless capsule endoscopy

More Related Videos

Microfluidic Flow Chambers Using Reconstituted Blood to Model Hemostasis and Platelet Transfusion In Vitro

Microfluidic Flow Chambers Using Reconstituted Blood to Model Hemostasis and Platelet Transfusion In Vitro

Published on: March 19, 2016

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Jan 4, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Microfluidic Flow Chambers Using Reconstituted Blood to Model Hemostasis and Platelet Transfusion In Vitro

Microfluidic Flow Chambers Using Reconstituted Blood to Model Hemostasis and Platelet Transfusion In Vitro

Published on: March 19, 2016

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Medical Imaging
Gastroenterology
Computer Vision

Background:

Wireless capsule endoscopy (WCE) generates extensive visual data for gastrointestinal tract inspection.
Manual review of WCE videos for abnormalities like bleeding is time-consuming and complex.
Existing automated bleeding detection methods using WCE images have limitations in feature extraction and dimensionality.

Purpose of the Study:

To develop an efficient and accurate automated bleeding detection scheme for WCE images.
To reduce computational complexity and improve the consistency of bleeding detection.
To identify the optimal probability density function (PDF) model for feature representation in bleeding detection.

Main Methods:

Pixels of interest (POI) are identified using a linear separation scheme.
Local spatial features are extracted from POI and a Rayleigh PDF model is fitted.
Fitted PDF parameters are used as features for a support vector machine (SVM) classifier.
An unsupervised clustering scheme refines bleeding region extraction.

Main Results:

The proposed PDF model fitting approach reduces computational complexity and enhances feature representation.
Rayleigh PDF model fitting to local spatial features demonstrated superior performance for bleeding detection.
The system achieved high performance metrics: 97.55% sensitivity, 96.59% specificity, and 96.77% accuracy.
Experimental analysis confirmed the effectiveness of PDF models, color spaces, and classifiers.

Conclusions:

The proposed PDF model fitting based approach offers a robust and efficient method for automated bleeding detection in WCE.
This method outperforms several state-of-the-art techniques in terms of accuracy and consistency.
The findings suggest a promising direction for improving automated analysis of WCE data.