Linear intensity normalization of DaTSCAN images using Mean Square Error and a model-based clustering approach

被引：6

作者：

Brahim, Abdelbasset ^{[1
]}

Manuel Gorriz, Juan ^{[1
]}

Ramirez, Javier ^{[1
]}

Khedher, Laila ^{[1
]}

机构：

[1] Univ Granada, Dept Signal Theory Networking & Commun, Granada, Spain

来源：

INNOVATION IN MEDICINE AND HEALTHCARE 2014 | 2014年 / 207卷

关键词：

Intensity normalization; DaTSCAN SPECT images; Mean Square Error; Gaussian Mixture Models; Parkinsonian syndrome; DISEASE PATIENTS;

D O I：

10.3233/978-1-61499-474-9-251

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The analysis of 3D SPECT brain images requires several pre-processing steps such as intensity normalization and brain feature extraction. In this sense, a new method for intensity normalization of (123) I-ioflupane-SPECT (DaTSCAN) brain images based on minimization of the Mean Square Error (MSE) between the Gaussian Mixture Model (GMM)-based extracted features from each subject image and a template in the so-defined non-specific region is derived. Our approach to feature extraction consists of using the set of parameters that define the template features, such as weights, covariance matrices and mean vectors to model the remaining images by reducing, consequently their dimensionality. The proposed method is compared to a widely used approach such as specific-to-non-specific binding ratio normalization. This comparison is performed on a DaTSCAN image database comprising analysis and classification stages for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome (PS) detection.

引用

页码：251 / 260

页数：10