Intensity Normalization of 123 I-ioflupane-SPECT Brain Images Using a Model-Based Multivariate Linear Regression Approach

Autor:	Laila Khedher, Juan Manuel Górriz, Abdelbasset Brahim, Javier Ramírez
Rok vydání:	2015
Předmět:	business.industry Computer science Speech recognition Pattern recognition CAD SPECT Brain Support vector machine Euclidean distance Intensity normalization Computer-aided diagnosis Bayesian multivariate linear regression Artificial intelligence business Divergence (statistics)
Zdroj:	Artificial Computation in Biology and Medicine ISBN: 9783319189130 IWINAC (1)
DOI:	10.1007/978-3-319-18914-7_8
Popis:	The intensity normalization step is essential, as it corresponds to the initial step in any subsequent computer-based analysis. In this work, a proposed intensity normalization approach based on a predictive modeling using multivariate linear regression (MLR) is presented. Different intensity normalization parameters derived from this model will be used in a linear procedure to perform the intensity normalization of 123 I-ioflupane-SPECT brain images. This proposed approach is compared to conventional intensity normalization methods, such as specific-to-non-specific binding ratio, integral-based intensity normalization and intensity normalization by minimizing the Kullback-Leibler divergence. For the performance evaluation, a statistical analysis is used by applying the Euclidean distance and the Jeffreys divergence. In addition, a classification task using support vector machine to evaluate the impact of the proposed methodology for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome detection.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4f2c66b179bbdad07070e356702ae160 https://doi.org/10.1007/978-3-319-18914-7_8 Zobrazit plný text záznamu