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

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Published on: November 30, 2022

Clinical content detection for medical image retrieval.

L Chen¹, H L Tang, I Wells

¹Department of Computing, University of Surrey, Guildford, Surrey, GU2 7XH, UK.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 7, 2007

Summary

This study introduces a novel Markov random field (MRF) model for detecting clinical features in histological images. This approach enables content-based image retrieval using clinical information, improving medical image analysis.

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Area of Science:

Medical Imaging
Computer Science
Pathology

Background:

Content-based image retrieval (CBIR) is common for medical images but limited for high-level clinical applications.
Current methods struggle with automatic detection of clinical features for content-based retrieval in large databases.

Purpose of the Study:

To develop a robust model for automatic clinical feature detection in large-scale histological image databases.
To enable content-based image retrieval using clinically relevant features.

Main Methods:

A Markov random field (MRF) based model was developed for clinical content detection.
Multiple classifiers were employed to recognize diverse clinical features.
Spatial contexts and local estimations were combined within the MRF for robust decision-making.

Main Results:

The MRF model demonstrated promising results in detecting clinical features in a large histological image database.
The approach facilitates image retrieval based on identified clinical content.

Conclusions:

The proposed MRF-based model effectively detects clinical features in histological images.
This method enhances the capability of content-based image retrieval for clinical applications in large medical image collections.