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Applying spatial distribution analysis techniques to classification of 3D medical images.

Abstract：

OBJECTIVE:The objective of this paper is to classify 3D medical images by analyzing spatial distributions to model and characterize the arrangement of the regions of interest (ROIs) in 3D space. METHODS AND MATERIAL:Two methods are proposed for facilitating such classification. The first method uses measures of similarity, such as the Mahalanobis distance and the Kullback-Leibler (KL) divergence, to compute the difference between spatial probability distributions of ROIs in an image of a new subject and each of the considered classes represented by historical data (e.g., normal versus disease class). A new subject is predicted to belong to the class corresponding to the most similar dataset. The second method employs the maximum likelihood (ML) principle to predict the class that most likely produced the dataset of the new subject. RESULTS:The proposed methods have been experimentally evaluated on three datasets: synthetic data (mixtures of Gaussian distributions), realistic lesion-deficit data (generated by a simulator conforming to a clinical study), and functional MRI activation data obtained from a study designed to explore neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). CONCLUSION:Performed experiments demonstrated that the approaches based on the KL divergence and the ML method provide superior accuracy compared to the Mahalanobis distance. The later technique could still be a method of choice when the distributions differ significantly, since it is faster and less complex. The obtained classification accuracy with errors smaller than 1% supports that useful diagnosis assistance could be achieved assuming sufficiently informative historic data and sufficient information on the new subject.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Pokrajac D,Megalooikonomou V,Lazarevic A,Kontos D,Obradovic Z

doi

10.1016/j.artmed.2004.07.001

subject

Has Abstract

pub_date

2005-03-01 00:00:00

pages

261-80

issue

3

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(04)00100-9

journal_volume

33

pub_type

杂志文章

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