Míriam López

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

Alzheimers 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.

Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease

In Alzheimers disease (AD) diagnosis process, functional brain image modalities such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians. However, the current evaluation of these images entails a succession of manual reorientations and visual interpretation steps, which attach in some way subjectivity to the diagnostic. In this work, a complete computer aided diagnosis (CAD) system for an automatic evaluation of the neuroimages is presented. Principal component analysis (PCA)-based methods are proposed as feature extraction techniques, enhanced by other linear approaches such as linear discriminant analysis (LDA) or the measure of the Fisher discriminant ratio (FDR) for feature selection. The final features allow to face up the so-called small sample size problem and subsequently they are used for the study of neural networks (NN) and support vector machine (SVM) classifiers. The combination of the presented methods achieved accuracy results of up to 96.7% and 89.52% for SPECT and PET images, respectively, which means a significant improvement over the results obtained by the classical voxels-as-features (VAF) reference approach.

A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI database

Several approaches appear in literature in order to develop Computed-Aided-Diagnosis (CAD) systems for Alzheimers disease (AD) detection. Although univariate models became very popular and nowadays they are widely used, recent investigations are focused on multivariate models which deal with a whole image as an observation. In this work, we compare two multivariate approaches that use different methodologies to relieve the small sample size problem. One of them is based on Gaussian Mixture Model (GMM) and models the Regions of Interests (ROIs) defined as differences between controls and AD subject. After GMM estimation using the EM algorithm, feature vectors are extracted for each image depending on the positions of the resulting Gaussians. The other method under study computes score vectors through a Partial Least Squares (PLS) algorithm based estimation and those vectors are used as features. Before extracting the score vectors, a binary mask based dimensional reduction of the input space is performed in order to remove low-intensity voxels. The validity of both methods is tested on the ADNI database by implementing several CAD systems with linear and nonlinear classifiers and comparing them with previous approaches such as VAF and PCA.

Automatic computer aided diagnosis tool using component-based SVM

Alzheimer type dementia (ATD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments and eventually causing death. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician’s diagnosis. However, conventional evaluation of these scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the Alzheimer’s disease. The proposed approach is based on a first automatic feature selection, and secondly a combination of component-based support vector machine (SVM) classification and a pasting votes technique of ensemble SVM classifiers.

Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer's disease

Finding sensitive and appropriate technologies for early detection of the Alzheimers disease (AD) are of fundamental importance to develop early treatments. Single Photon Emission Computed Tomography (SPECT) images are non-invasive observation tools to assist the diagnosis, commonly processed through unsupervised statistical tests, or assessed visually. In this work, we present a computer aided diagnosis system based on supervised learning methods, exploring two different novel approaches. Independent Component Analysis (ICA) was used within this work to extract the relevant features from the image database and reduce the feature space dimensionality, to build a SVM with the resulting data. The proposed approach led to an error estimation below the 9%, and was able to detect the AD perfusion pattern and classify new subjects in an unsupervised manner.

Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines.

This paper presents a computer-aided diagnosis technique for improving the accuracy of diagnosing the Alzheimers type dementia. The proposed methodology is based on the calculation of the skewness for each m-by-m-by-m sliding block of the SPECT brain images. The center pixel in this m-by-m-by-m block is replaced by the skewness value to build a new 3-D brain image which is used for classification purposes. After that, voxels which present a Welchs t-statistic between classes, Normal and Alzheimers images, higher (or lower) than a threshold are selected. The mean, standard deviation, skewness and kurtosis are calculated for these selected voxels and they are subjected as features to linear kernel based support vector machine classifier. The proposed methodology reaches accuracy higher than 99% in the classification task.

Early Detection of the Alzheimer Disease Combining Feature Selection and Kernel Machines

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. However, currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinicians diagnosis. However, conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on feature selection and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach attaining a 90% AD diagnosis accuracy.

Alzheimer's Diagnosis Using Eigenbrains and Support Vector Machines

An accurate and early diagnosis of the Alzheimers Disease (AD) is of fundamental importance for the patients medical treatment. Single Photon Emission Computed Tomography (SPECT) images are commonly used by physicians to assist the diagnosis, rating them by visual evaluations. In this work we present a computer assisted diagnosis tool based on a Principal Component Analysis (PCA) dimensional reduction of the feature space approach and a Support Vector Machine (SVM) classification method for improving the AD diagnosis accuracy by means of SPECT images. The most relevant image features were selected under a PCA compression, which diagonalizes the covariance matrix, and the extracted information was used to train a SVM classifier which could classify new subjects in an unsupervised manner.

Automatic System for Alzheimer's Disease Diagnosis Using Eigenbrains and Bayesian Classification Rules

Alzheimers Disease (AD) is a progressive neurologic disease of the brain that leads to the irreversible loss of neurons and dementia. The new brain imaging techniques PET (Positron Emission Tomography) and SPECT (Single Photon Emission Computed Tomography) provide functional information about the brain activity and have been widely used in the AD diagnosis process. However, the diagnosis currently relies on manual image reorientations, visual evaluation and other subjective, time consuming steps. In this work, a complete computer aided diagnosis (CAD) system is developed to assist the clinicians in the AD diagnosis process. It is based on bayesian classifiers, made up from features previously extracted. The small size sample problem, consisting of having a number of available samples much lower than the dimension of the feature space, is faced up by applying Principal Component Analysis (PCA) to the features. This approach provides higher accuracy values than other previous approaches do, yielding 91.21% and 98.33% accuracy values for SPECT and PET images, respectively.

Early Alzheimer's disease diagnosis using partial least squares and random forests

Currently, the accurate diagnosis of the Alzheimer disease (AD) still remains a challenge in the clinical practice. This paper shows a novel computer aided diagnosis (CAD) system for the early Alzheimers disease using single photon emission computed tomography (SPECT) images. The proposed system combines a partial least square (PLS) regression model for feature extraction and a random forest predictor. The generalization error of the random forest classifier converges to a limit as the number of trees in the forest increases. PLS feature extraction is found to be more effective for obtaining discriminant information from the data and outperforms principal component analysis (PCA) as a feature extraction technique yielding peak values of sensitivity=100%, specificity= 92.7% and accuracy= 96.9%. Moreover, the proposed CAD system outperformed recently developed AD CAD systems.