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Content-based medical image classification using a new hierarchical merging scheme.

Hossein Pourghassem¹, Hassan Ghassemian

¹School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran. h poorghasem@modares.ac.ir

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

|September 16, 2008

Summary

This study introduces a hierarchical method for automatic medical image classification, improving content-based image retrieval (CBIR) accuracy. The novel approach achieves 90.83% accuracy on X-ray images, enhancing diagnostic capabilities.

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Area of Science:

Medical Imaging
Computer Science
Artificial Intelligence

Background:

Automatic medical image classification is crucial for content-based image retrieval (CBIR) but faces computational complexity.
Existing methods struggle with overlapping classes and require efficient feature extraction.

Purpose of the Study:

To propose a novel hierarchical medical image classification method to address computational complexity and improve CBIR.
To introduce a new merging scheme and spectral/directional features for enhanced classification accuracy.

Main Methods:

A two-level hierarchical classification approach using shape, texture, tessellation-based spectral, and directional histogram features.
A merging scheme employing accuracy, miss-classified ratio, and dissimilarity measures to identify and merge overlapping classes.
Multilayer perceptron (MLP) classifiers used in a merging-based classification strategy within each hierarchical level.

Main Results:

The proposed method achieved an accuracy rate of 90.83% on 25 merged classes in the first level of classification.
When considering the correct class within the top three matches, the accuracy increased to 97.9%.
The hierarchical approach effectively created homogenous semantic classes from overlapping ones.

Conclusions:

The developed hierarchical medical image classification method offers a significant improvement in accuracy for CBIR systems.
The novel merging scheme and feature extraction techniques provide a robust solution for complex medical image datasets.
This approach demonstrates potential for enhancing the efficiency and reliability of automated medical image analysis.