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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Synthesis and herbicidal activity of optically active α-(substituted phenoxyacetoxy) (substituted phenyl) methylphosphonates.

Pesticide biochemistry and physiology·2017
Same author

Structural basis for the recognition of kinesin family member 21A (KIF21A) by the ankyrin domains of KANK1 and KANK2 proteins.

The Journal of biological chemistry·2017
Same author

S149R, a novel mutation in the <i>ABCD1</i> gene causing X-linked adrenoleukodystrophy.

Oncotarget·2017
Same author

Transgenic cotton co-expressing chimeric Vip3AcAa and Cry1Ac confers effective protection against Cry1Ac-resistant cotton bollworm.

Transgenic research·2017
Same author

Effective adsorption of nitroaromatics at the low concentration by a newly synthesized hypercrosslinked resin.

Water science and technology : a journal of the International Association on Water Pollution Research·2017
Same author

Comparative Genome Analysis Reveals Adaptation to the Ectophytic Lifestyle of Sooty Blotch and Flyspeck Fungi.

Genome biology and evolution·2017

Related Experiment Video

Updated: May 6, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

5.8K

Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma

Shan Hu1, Chao Xu, Weiqiao Guan

  • 1Department of Biomedical Engineering, ZhongShan School of Medicine, Sun Yat-Sen University, GuangZhou, 510060, PR China.

Bio-Medical Materials and Engineering
|November 12, 2013
PubMed
Summary

This study used wavelet transforms for osteosarcoma (bone cancer) detection in CR images. Wavelet analysis showed higher accuracy than GLCM, proving its value in computer-aided diagnosis.

Keywords:
Feature selectiongray-level co-occurrence matrixosteosarcoma diagnosistexture featurewavelet transform

More Related Videos

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

4.4K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

8.7K

Related Experiment Videos

Last Updated: May 6, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

5.8K
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

4.4K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

8.7K

Area of Science:

  • Medical imaging
  • Oncology
  • Computer-aided diagnosis

Background:

  • Osteosarcoma is a common bone cancer in children and adolescents.
  • Accurate diagnosis is crucial for effective treatment.
  • Current diagnostic methods can be enhanced by advanced image analysis techniques.

Purpose of the Study:

  • To evaluate the effectiveness of image texture analysis for osteosarcoma recognition.
  • To compare the performance of Sym4 and Db4 wavelet transforms against Gray-Level Co-occurrence Matrices (GLCM) for feature extraction.
  • To assess the utility of these methods in computer-aided diagnosis systems.

Main Methods:

  • Texture features were extracted from bone CR images using Sym4 and Db4 wavelet transforms and GLCM.
  • Statistical methods were employed for optimal feature selection.
  • A support vector machine algorithm was used to evaluate classification performance.

Main Results:

  • Sym4 wavelet achieved 93.44% accuracy for epiphyseal osteosarcoma, while Db4 wavelet achieved 96.25% for diaphyseal osteosarcoma.
  • Wavelet-derived features yielded superior accuracy, sensitivity, specificity, and ROC curves compared to GLCM.
  • Multi-resolution analysis proved effective for texture feature extraction in bone CR images.

Conclusions:

  • Texture features extracted using wavelet transforms are valuable for computer-aided osteosarcoma diagnosis.
  • Wavelet transforms offer a robust approach for analyzing bone CR images.
  • Multi-resolution analysis is a key technique for enhancing texture feature extraction in medical imaging.