Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features

• Published in: Information sciences Vol. 237; pp. 59 - 72
• Main Authors: Ramírez, J., Górriz, J.M., Salas-Gonzalez, D., Romero, A., López, M., Álvarez, I., Gómez-Río, M.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 10.07.2013
• Subjects: 42.30.Sy; 87.19.xr; 87.57.R; Alzheimer’s type dementia; Computer-aided diagnosis; Feature extraction; Support vector machines; Support vector machines; 87.19.xr; Feature extraction; 87.57.R; Alzheimer’s type dementia; Computer-aided diagnosis; 42.30.Sy
• ISSN: 0020-0255; 1872-6291; 1872-6291
• DOI: 10.1016/j.ins.2009.05.012

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. With the growth of the older population in developed nations, the prevalence of AD is expected to triple over the next 50 years while its early diagnosis remains being a difficult task. Functional imaging modalities including Single Photon Emission Computed Tomography (SPECT) and positron emission tomography (PET) are often used with the aim of achieving early diagnosis. However, conventional evaluation of SPECT images often relies on manual reorientation, visual reading of tomographic slices and semiquantitative analysis of certain regions of interest (ROIs). These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the early detection of the AD. The proposed approach is based on image parameter selection and support vector machine (SVM) classification. A study is carried out in order to finding the ROIs and the most discriminant image parameters with the aim of reducing the dimensionality of the input space and improving the accuracy of the system. Among all the features evaluated, coronal standard deviation and sagittal correlation parameters are found to be the most effective ones for reducing the dimensionality of the input space and improving the diagnosis accuracy when a radial basis function (RBF) SVM is used. The proposed system yields a 90.38% accuracy in the early diagnosis of the AD and outperforms existing techniques including the voxel-as-features (VAF) approach.