Related Experiment Video
Updated: Apr 25, 2026
08:40
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
Published on: April 8, 2016
12.4K
A survey of prostate modeling for image analysis
O Chilali1, A Ouzzane2, M Diaf3
1Inserm U703, 152, rue du Docteur Yersin, Lille University Hospital, 59120 Loos, France; Automatic Department, Mouloud Mammeri University, Tizi-Ouzou, Algeria.
Computers in Biology and Medicine
|August 27, 2014
Summary
Computer technology aids multimodal prostate imaging analysis by extracting organ features. This review classifies knowledge modeling techniques and surveys their clinical applications.
Area of Science:
- Medical Imaging
- Computer Science
- Biomedical Engineering
Background:
- Multimodal image analysis is crucial for prostate gland assessment.
- Computer technology integration is advancing prostate imaging techniques.
- Existing methods often rely on a priori knowledge from organ features.
Purpose of the Study:
- To review the steps involved in knowledge extraction and modeling for prostate image analysis.
- To classify modeling techniques based on data analysis methods and features used.
- To survey the clinical applications of these advanced imaging techniques.
Main Methods:
- Review of existing literature on multimodal prostate image analysis.
- Classification of knowledge extraction and modeling approaches.
- Categorization based on data analysis methods (e.g., machine learning, statistical modeling).
- Feature-based classification (e.g., shape, texture, intensity features).
Main Results:
- Identification and categorization of various knowledge extraction and modeling strategies.
- Understanding of how different data analysis methods and features impact model performance.
- Overview of the current landscape of clinical applications for these technologies.
Conclusions:
- Multimodal image analysis using computer technology offers significant potential for prostate gland assessment.
- A systematic classification of modeling techniques is essential for advancing the field.
- Further research into clinical applications will drive the adoption of these advanced methods.
