Subject: Medical Cases, Medical Images, MRI, Image Retrieval, Longitudinal Data, Static Features, Dynamic Features, SPARE-AD, Alzheimer’s Disease
Year: 2022
Type: Proceedings
Title: Combining Static and Dynamic Features to Improve Longitudinal Image Retrieval for Alzheimer's Disease
Author: Trojachanec Dineva, Katarina
Author: Kitanovski, Ivan
Author: Dimitrovski, Ivica
Author: Loshkovska, Suzana
Abstract: The aim of this paper is to enhance medical case retrieval for Alzheimer’s disease on the basis of the domain knowledge. We approached the problem in a longitudinal manner, and we represented the medical cases by using different kind of information extracted from Magnetic Resonance Images (MRI) aiming to improve the semantic relevance, precision and efficiency of the retrieval. More particularly, we evaluated the combination of the static, dynamic features and the index reflecting the spatial pattern of abnormality (SPARE-AD) for representing the longitudinal images. According to the obtained results, the combination of the static features representing the volumetric measures along with the cortical thickness measures of the brain structures at the later time point/s together with the dynamic features such as percent change with respect to the value obtained from the linear fit at baseline and symmetrized percent change of the volumetric measures, as well as the index of abnormality provided the best overall retrieval results. The dimensionality of the feature vector was 31-33 features in most of the cases which is significantly lower than in the case of the traditional approach (thousands features in the cases when the whole brain is considered). The approach based on a combination of different kinds of features extracted from the longitudinal data, suggested in this paper, corresponds directly to the nature of the application domain and provides powerful results, yet effective and efficient way for MRI retrieval for AD.
Publisher: Springer Nature Switzerland
Relation: 14th International Conference, ICT Innovations 2022
Identifier: oai:repository.ukim.mk:20.500.12188/26374
Identifier: http://hdl.handle.net/20.500.12188/26374
