Research Repository

Medical image modality classification using discrete Bayesian Networks

Martinez-Gomez, J and Arias, J and Seco de Herrera, AG and Müller, H (2016) 'Medical image modality classification using discrete Bayesian Networks.' Computer Vision and Image Understanding, 151. 61 - 71. ISSN 1077-3142

[img]
Preview
Text
bayesian.pdf - Accepted Version

Download (1MB) | Preview

Abstract

In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifiers used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes.

Item Type: Article
Uncontrolled Keywords: Medical image analysis, Visual features extraction, Bayesian networks, Hierarchical classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 04 Jan 2018 16:27
Last Modified: 04 Jan 2018 17:15
URI: http://repository.essex.ac.uk/id/eprint/20944

Actions (login required)

View Item View Item