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

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

You might also read

Related Articles

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

Sort by

Same author

A phylogeny-guided framework for decoding mechanisms of human endogenous retrovirus regulation in health and disease.

bioRxiv : the preprint server for biology·2026

Same author

Distributed event-triggered robust adaptive formation control of multi-agent rigid bodies on [Formula: see text].

Scientific reports·2026

Same author

BenchDrop-seq: a microfluidics-free platform for benchtop single-cell long-read RNA sequencing.

bioRxiv : the preprint server for biology·2026

Same author

EBV infection outcomes determined by monocyte and TREG-driven immune dynamics in an ex vivo pbmc model.

PLoS pathogens·2026

Same author

Potential benefits of <i>Pterocarpus marsupium</i> heartwood extract on high fat diet induced rodent model of atherosclerosis.

Archives of physiology and biochemistry·2026

Same author

Utility of PMMA-Based Cranioplasty Flap for the Reconstruction of Retromastoid Craniotomy Using a Single Standard Skull Model: A Low-Cost Alternative.

Asian journal of neurosurgery·2025

Search research articles

Related Experiment Video

Updated: May 2, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Texture-based medical image retrieval in compressed domain using compressive sensing.

Kuldeep Yadav¹, Avi Srivastava¹, Ankush Mittal²

¹Computer Science Department, College of Engineering Roorkee, Roorkee, India.

International Journal of Bioinformatics Research and Applications

|March 5, 2014

Summary

This summary is machine-generated.

This study introduces a novel texture-based image retrieval model using compressive sensing for efficient medical image database searching. It enables faster, higher-quality image recovery from fewer samples.

Keywords:

DCT acquisition speed basis pursuit algorithm compressed domain image retrieval compressive sampling compressive sensing content based image retrieval discrete cosine transform image quality medical images medical imaging texture based image retrieval

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

Related Experiment Videos

Last Updated: May 2, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

Area of Science:

Computer Science
Medical Imaging
Signal Processing

Background:

Content-based image retrieval (CBIR) is crucial for managing large datasets.
Texture analysis is a key component in CBIR systems.
Medical imaging generates vast databases, making retrieval challenging.

Purpose of the Study:

To develop an efficient texture-based image retrieval model for compressed medical images.
To leverage compressive sensing for improved image recovery and database management.

Main Methods:

Utilizing DC coefficients from compressed images.
Implementing compressive sensing (CS) for image sampling and reconstruction.
Focusing on texture features for retrieval accuracy.

Main Results:

Demonstrated accurate image recovery from significantly fewer samples.
Achieved increased acquisition speed and enhanced image quality.
Showcased the model's effectiveness in texture-based retrieval from compressed domains.

Conclusions:

Compressive sensing offers a powerful approach for efficient texture-based image retrieval in medical imaging.
The proposed model addresses the challenges of large medical image databases by enabling faster and more accurate retrieval.