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

Deep learning based hair removal on ultraviolet-induced fluorescence dermatoscopy images.

Computer methods and programs in biomedicine·2026

Same author

Genetically engineered probiotic E. coli Nissle 1917 enhances protection against Salmonella via increased adhesion and systemic T-cell responses.

NPJ biofilms and microbiomes·2026

Same author

A multimodal data acquisition and registration system for monitoring skin wounds: Wound modeling and accuracy estimation using phantoms.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same author

Radiomic feature-based classification of BI-RADS 4/5 breast lesions on contrast-enhanced mammography.

Computer methods and programs in biomedicine·2026

Same author

Actinic keratosis staging in multimodal image data.

Computer methods and programs in biomedicine·2026

Same author

Objective Dynamic Assessment of Facial Movement Asymmetry in Children Using a Marker-Based Video Method.

Journal of clinical medicine·2026

Same journal

Continual test-time adaptation via weight averaging of feature augmentations in cross-domain medical image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

A lightweight network for segmenting tree-like structures in medical images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

Self-supervised isotropic reconstruction for abnormality detection in anisotropic MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

ScribSAM: A robust scribble-supervised framework for spatiotemporal segmentation of breast lesions in ultrasound videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone

Published on: September 9, 2020

A new parametric model-based technique in bone tumour analysis.

Joanna Czajkowska¹, Ewa Pietka²

¹Department of Computer Science and Medical Equipment, Faculty of Biomedical Engineering, Silesian University of Technology, ul. Charlesa de Gaulle'a 66, 41-800 Zabrze, Poland; Institute for Vision and Graphics, University of Siegen, Hoerlindstr. 3, 57076 Siegen, Germany.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|May 13, 2014

Summary

This summary is machine-generated.

This study introduces a novel statistical model for segmenting inhomogeneous bone tumors using Gaussian mixture models and fuzzy connectedness. The new method significantly improves segmentation accuracy in MRI scans compared to existing techniques.

Keywords:

3-D segmentation Bone tumours Fuzzy connectedness analysis Gaussian mixture model Magnetic resonance imaging

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

Models of Bone Metastasis

Models of Bone Metastasis

Published on: September 4, 2012

Related Experiment Videos

Last Updated: Apr 30, 2026

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone

Published on: September 9, 2020

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

Models of Bone Metastasis

Models of Bone Metastasis

Published on: September 4, 2012

Area of Science:

Medical imaging analysis
Biomedical engineering
Radiology

Background:

Accurate segmentation of inhomogeneous bone tumors is crucial for diagnosis and treatment planning.
Existing segmentation methods often struggle with the complex and variable structures of bone tumors.
Advanced statistical modeling offers potential for improved segmentation accuracy.

Purpose of the Study:

To develop and evaluate a novel statistical model-based segmentation technique for inhomogeneous bone tumors.
To enhance the accuracy and reliability of bone tumor structure analysis in medical imaging.
To compare the performance of the new technique against conventional and existing segmentation methods.

Main Methods:

A 3-D segmentation procedure utilizing a Gaussian mixture model for statistical structure description.
An adaptive model-based relative fuzzy connectedness technique for segmentation.
Validation on 94 MRI series from 38 young patients with bone tumors.

Main Results:

The developed technique demonstrated higher bone tumor segmentation accuracy.
Results surpassed those obtained with conventional fuzzy connectedness approaches.
The method outperformed other literature-based segmentation techniques, including active contour models and statistical analysis.

Conclusions:

The proposed statistical model-based segmentation technique offers superior accuracy for inhomogeneous bone tumors.
This advancement can improve diagnostic capabilities and treatment planning for bone tumors.
The technique shows promise for clinical application in radiological image analysis.