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

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by

Same author

Deep Learning Based COVID-19 Detection Using Medical Images: Is Insufficient Data Handled Well?

Current medical imaging·2022

Same author

Review on Deep Learning Methodologies in Medical Image Restoration and Segmentation.

Current medical imaging·2022

Same author

Exploration of a Framework for the Identification of Chronic Kidney Disease Based on 2D Ultrasound Images: A Survey.

Current medical imaging·2020

Same author

A Review on Lossless Compression Techniques for Wireless Capsule Endoscopic Data.

Current medical imaging·2020

Same author

Altered Cortical and Postural Response to Balance Perturbation in Traumatic Brain Injury - An EEG Pilot Study<sup>.</sup>

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020

Same author

Unsupervised Stochastic Strategies for Robust Detection of Muscle Activation Onsets in Surface Electromyogram.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2018

Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026

Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026

Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026

Same journal

A robot-assisted eye positioning method with high precision and repeatability for ocular particle therapy: mechanical and geometric assessment.

Medical & biological engineering & computing·2026

Same journal

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026

Same journal

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Medical & biological engineering & computing·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 20, 2026

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

Neighbourhood search feature selection method for content-based mammogram retrieval.

D Abraham Chandy¹, A Hepzibah Christinal², Alwyn John Theodore²

¹Karunya University, Coimbatore, Tamil Nadu, India. abrahamdchandy@gmail.com.

Medical & Biological Engineering & Computing

|June 6, 2016

Summary

This summary is machine-generated.

This study introduces a neighborhood search method for selecting optimal feature subsets in mammogram retrieval. The new approach significantly improves precision and reduces feature dimensions for better diagnostic support.

Keywords:

Content-based image retrieval Feature selection Mammogram Neighbourhood search Precision

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Related Experiment Videos

Last Updated: Mar 20, 2026

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

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Content-based image retrieval (CBIR) is crucial for clinical diagnosis support.
Mammogram analysis requires efficient feature selection for accurate retrieval.

Purpose of the Study:

To propose a neighborhood search method for near-optimal feature subset selection in mammogram retrieval.
To enhance the performance of content-based image retrieval systems for diagnostic purposes.

Main Methods:

Extracted features from mammograms using Grey Level Co-occurrence Matrix, Daubechies-4 wavelet, Gabor, Cohen-Daubechies-Feauveau 9/7 wavelet, and Zernike moments.
Developed and applied a neighborhood search algorithm for feature subset selection.
Evaluated performance using precision, storage requirements, and retrieval time.

Main Results:

Achieved significant improvements in mean precision rate.
Demonstrated a reduction in feature dimension.
The proposed method outperformed existing state-of-the-art feature selection techniques.

Conclusions:

The neighborhood search method offers a superior approach for feature selection in mammogram retrieval.
This method enhances the efficiency and accuracy of computer-aided diagnosis systems.
Optimized feature subsets lead to improved diagnostic support through better image retrieval.