Ignacio Alvarez Illan

• PhD
• Professor (Associate) at University of Granada

Finding sensitive and appropriate technologies for non-invasive observation and early detection of Alzheimer’s disease (AD) is of fundamental importance to develop early treatments. In this work we develop a fully automatic computer aided diagnosis (CAD) system for high-dimensional pattern classification of baseline 18F-FDG PET scans from Alzheimer...

Purpose In this work, an approach to computer aided diagnosis (CAD) system is proposed as a decision‐making aid in Parkinsonian syndrome (PS) detection. This tool, intended for physicians, entails fully automatic preprocessing, normalization, and classification procedures for brain single‐photon emission computed tomography images. Methods Ioflupa...

This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD. The structure of relations betwe...

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's d...

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new...

Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes...

Autism Spectrum Condition (ASC) is a neurodevelopmental condition characterized by impairments in communication, social interaction and restricted or repetitive behaviors. Extensive research has been conducted to identify distinctions between individuals with ASC and neurotypical individuals. However, limited attention has been given to comprehensi...

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tas...

The stable supply of electricity is essential for the industrial activity and economic development as well as for human welfare. For this reason, electrical system devices are equipped with monitoring systems that facilitate their management and ensure an uninterrupted operation. This is the case of electrical power transformers, which usually have...

This work proposes the use of 3D convolutional variational autoencoders (CVAEs) to trace the changes and symptomatology produced by neurodegeneration in Parkinson's disease (PD). In this work, we present a novel approach to detect and quantify changes in dopamine transporter (DaT) concentration and its spatial patterns using 3D CVAEs on Ioflupane (...

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). Deep Learning (DL) algorithms are grounded in non-linear and complex artificial neural systems that progressively extract higher-level features from data. DL is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. It is al...

New societal challenges due to the climate emergency will change countries’ energy systems in the short and medium term. In this context, electrical energy and its production, distribution, transformation and storage will play a decisive role. The irruption of the electric car and the increased use of renewable production sources (of an intermitten...

Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involv...

The detection of Alzheimer’s Disease in its early stages is crucial for patient care and drugs development. Motivated by this fact, the neuroimaging community has extensively applied machine learning techniques to the early diagnosis problem with promising results. The organization of challenges has helped the community to address different raised...

In the immediate future, with the increasing presence of electrical vehicles and the large increase in the use of renewable energies, it will be crucial that distribution power networks are managed, supervised and exploited in a similar way as the transmission power systems were in previous decades. To achieve this, the underlying infrastructure re...

Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning di...

Imbalanced datasets constitute a challenge in medical-image processing and machine learning in general. When the available training data is highly imbalanced, the risk for a classifier to find the trivial solution increases dramatically. To control the risk, an estimate on the prior class probabilities is usually required. In some medical datasets,...

Imbalanced datasets often pose challenges in classification problems. In this work we study and quantify the problem of imbalanced classification using support vector machines (SVM). We identify the conditions under which a SVM failure occur, both theoretically and experimentally, and show that it can be relevant even in cases of very weakly imbala...

In recent years, the use of I[123]-FP-CIT or I[123]-Ioflupane SPECT images has emerged as an effective support tool for Parkinson’s Disease diagnosis. Many works in this field have consisted on comparing different images obtained from subjects both Healthy Control (HC) subjects and patients with Parkinsonism (PD) and using them to obtain measures (...

Accurate methods for computer aided diagnosis of breast cancer increase accuracy of detection and provide support to physicians in detecting challenging cases. In dynamic contrast enhancing magnetic resonance imaging (DCE-MRI), motion artifacts can appear as a result of patient displacements. Non-linear deformation algorithms for breast image regis...

Computer aided applications in Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) are increasingly gaining attention as important tools to asses the risk of breast cancer. Chest wall detection and whole breast segmentation require effective solutions to increase the potential benefits of computer aided tools for tumor detection. Here we...

Non-mass enhancing lesions (NME) constitute a diagnostic challenge in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer Aided Diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new...

Background Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10–15% per year. However, t...

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In...

Python code for the synthesis of nuclear brain imaging. Read the documentation at http://brainsimulator.readthedocs.io

The rise of neuroimaging in research and clinical practice, together with the development of new machine learning techniques has strongly encouraged the Computer Aided Diagnosis (CAD) of different diseases and disorders. However, these algorithms are often tested in proprietary datasets to which the access is limited and, therefore, a direct compar...

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strat...

This paper deals with the topic of learning from unlabeled or noisy-labeled data in the context of a classification problem. In the classification problem the outcome yields one of a discrete set of values thus, assumptions on them could be established to obtain the most likely prediction model at the training stage. In this paper a novel case-base...

Many advanced automated systems have been proposed for the diagnosis of Alzheimer's Disease (AD). Most of them use Magnetic Resonance Imaging (MRI) as input data, since it provides high resolution images of the structure of the brain. Usually, Computer Aided Diagnosis (CAD) systems are based on massive univariate test and classification, although m...

\(^{123}\)I-ioflupane single photon emission computed tomography (SPECT) is a standard and well-known imaging modality in the medical practice for the diagnosis of Parkinson’s disease (PD). That said, atypical parkinsonian syndrome (APS), a symptom-related disease to PD, detection is yet considered inconsistent at least based on visual inspection o...

Machine learning has been successfully applied to many areas of science and engineering. Some examples include time series prediction, optical character recognition, signal and image classification in biomedical applications for diagnosis and prognosis, etc. In the theory of semi-supervised learning, we have a training set and an unlabeled data tha...

Slides for "A Volumetric Radial LBP Projection of MRI Brain Images for the Diagnosis of Alzheimer’s Disease"

Parkinsonism is the second most common neurodegenerative disease, originated by a dopamine decrease in the striatum. Single Photon Emission Computed Tomography (SPECT) images acquired using the DaTSCAN drug are a widely extended tool in the diagnosis of Parkinson's Disease (PD), since they can measure the amount of dopamine transporters in the stri...

In this work, we normalize the intensity of 40 FP-CIT SPECT images from the Parkinson’s Progression Markers Initiative assuming that the histogram of intensity values follows an \(\alpha \)-stable distribution. Then, we study the normalized images. The interclass separation of the Parkinson’s disease (PD) brain images and the healthy control (HC) a...

Parkinsonism is the second most common neurodegenerative disease, originated by a dopamine decrease in the striatum. Single Photon Emission Computed Tomography (SPECT) images acquired using the DaTSCAN drug are a widely extended tool in the diagnosis of Parkinson’s Disease (PD), since they can measure the amount of dopamine transporters in the stri...

Parkinsonism is the second more common neurological disease and affects around 1%–2% of people over 65 years, being around 20%–24% of them incorrectly diagnosed. The disorder is associated to a progressive loss of dopaminergic neurons of the striatum. Thus, its diagnosis is usually corroborated by analyzing neuroimaging data of this region. In this...

In the theory of semi-supervised learning, we have a training set and a unlabeled data that are employed to fit a prediction model or learner with the help of an iterative algorithm such as the expectation-maximization (EM) algorithm. In this paper a novel non-parametric approach of the so called case-based statistical learning in a low-dimensional...

2013 IEEE. Machine learning has been successfully applied to many areas of science and engineering. Some examples include time series prediction, optical character recognition, signal and image classification in biomedical applications for diagnosis and prognosis and so on. In the theory of semi-supervised learning, we have a training set and an un...

Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a...

Alzheimer’s Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are u...

Alzheimer’s Disease (AD) is nowadays the most common type of dementia, with more than 35.6 million people affected, and 7.7 million new cases every year. Magnetic Resonance Imaging (MRI) is a fairly widespread tool used in clinical practice, and has repeatedly proven its utility in the diagnosis of AD. Therefore a number of automatic methods have b...

An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance to improve diagnosis techniques, to better understand this neurodegenerative process and to develop effective treatments. In this work, a novel classification method based on independent component analysis (ICA) and supervised learning methods is proposed t...

Spatial affine registration of brain images to a common template is usually performed as a preprocessing step in intersubject and intrasubject comparison studies, computeraided diagnosis, region of interest selection and brain segmentation in tomography. Nevertheless, it is not straightforward to build a template of [123I]FP-CIT SPECT brain images...

In this work, we perform a comparison between the spatial normalization of [123I]FP-CIT SPECT brain images when a FP-CIT SPECT and a MRI template are used. A 12-parameters affine registration model is calculated by the optimization of a sum of squares cost function. When the images are registered to a FP-CIT template, the intersubject variation is...

This paper presents the analysis of the statistical significance in the selection of the ROI for the discriminant analysis of brain images to identify Parkinson patients or subjects without any pathology. The particular features and brain functional patterns of the Parkinson's disease cause that there are regions that conveniently reveal the presen...

Recent advances in the process of diagnosis of neurodegenerative diseases, such as Alzheimer's Disease, rely on the use of molecular imaging that allow the interpretation of different metabolic biomarkers in the brain. However these procedures are considered of invasive nature, as they involve the injection of radioactive markers. On the other hand...

The study of neurodegenerative diseases has been based for some time on visual and semi-quantitative analysis of medical imaging. This is the case of Parkinsonian Syndrome (PS) or Parkinsonism, which is the second most common neurodegenerative disorder, where 123I-ioflupane (better known by its tradename, DaTSCAN) images have been of great help. Re...

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 modaliti...

An automated method for orientation of functional brain image is proposed. Intrinsec information is captured from the image in three stages: first the volume to identify the anterior to posterior line, second the symmetry to detect the hemisphere dividing plane and third the contour to determine the up-down and front-back orientation. The approach...

In this document, a complete computer aided diagnosis procedure is presented, for the early diagnosis of Parkinson’s disease. The method is applied to single-photon emission computed tomography (SPECT) brain images in order to identify parkinsonian patterns on them. Two strategies are proposed for the identification: a nearest neighbors classificat...

In this work, a novel approach to Computer Aided Diagnosis (CAD) system for the Parkinson’s Disease (PD) is proposed. This tool is intended for physicians, and is based on fully automated methods that lead to the classification of Ioflupane/FP-CIT-I-123 (DaTSCAN) SPECT images. DaTSCAN images from the Parkinson Progression Markers Initiative (PPMI)...

A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source...

Objective: This paper explores the importance of the latent symmetry of the brain in computer-aided systems for diagnosing Alzheimer's disease (AD). Symmetry and asymmetry are studied from two points of view: (i) the development of an effective classifier within the scope of machine learning techniques, and (ii) the assessment of its relevance to...

Alzheimer and Parkinson Diseases are the two most common neurodegenerative disorders. As the number of AD and PD patients has increased, its early diagnosis has received more attention for both social and medical reasons. Single photon emission computed tomography (SPECT), measuring the regional cerebral blood flow, enables the diagnosis even befor...

In this work, a procedure to perform the normalization of the intensity values in FP-CIT SPECT images for the diagnosis of Parkinson's disease is presented. The proposed methodology is based on the fact that the shape of the distribution of intensity values is skewed and heavy-tailed, and therefore, it can be modelled in a parsimonious way using th...

This work shows an Association Rule (AR)-based approach in order to design a computer aided diagnosis (CAD) system for the Alzheimer's disease (AD) detection with a 18F-FDG and Pittsburg Compound B (PiB) PET (Positron Emission Tomography) biomarker analysis. The AD Neuroimaging Initiative (ADNI) 1 dataset is used for testing and a comparison is con...

This letter shows a novel computer aided diagnosis (CAD) system for the early diagnosis of Alzheimer’s Disease (AD). The proposed method evaluates the reliability of association rules (ARs) aiming to discover interesting associations between attributes in functional brain imaging, i.e. single photon emission computed tomography (SPECT) and positron...

Background Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer’s Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems....

An accurate and early diagnosis of Parkinsonian syndrome (PS) is nowadays a challenge. This syndrome includes several pathologies with similar symptoms (Parkinson's disease, multisystem atrophy, progressive supranuclear palsy, corticobasal degeneration and others) which make the diagnosis more difficult. (123)I-ioflupane allows to obtain in vivo im...

This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two differen...

Imaging of dopamine transporters (DaT) has been introduced as a valuable tool to evaluate patients with several neuropsychiatrie disorders, such as Parkinson's disease (PD). Over the last decade, several computer applications have been developed in order to facilitate the exploration of DaT images to clinicians, however they require a high interact...

In this paper we propose a novel method for brain SPECT image feature extraction based on the Empirical Mode Decomposition (EMD). The proposed method applied to assist the diagnosis of Alzheimer Disease (AD) selects the most discriminant voxels for classification from the transformed EMD feature space. In particular, high-frequency components of th...

In this paper, a novel technique based on association rules (ARs) is presented in order to find relations among activated brain areas in single photon emission computed tomography (SPECT) imaging. In this sense, the aim of this work is to discover associations among attributes which characterize the perfusion patterns of normal subjects and to make...

In this paper we present a novel classification method of SPECT images for the development of a computer aided diagnosis (CAD) system aiming to improve the early detection of the Alzheimer’s Disease (AD). The system combines firstly template-based normalized mean square error (NMSE) features of tridimensional Regions of Interest (ROIs) t-test selec...

In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer’s disease (AD). The proposed method is based on distance metric learning classification with the Large Margin Nearest Neighbour algorithm (LMNN) aiming to separate examples from different classes (Normal and AD) by a large margin. In part...

In Alzheimer's 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 step...

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioural impairments and eventually causing death. Functional brain imaging as single-photon emission computed tomography (SPECT) is commonly used to guide the clinician’s diagnos...

Purpose: This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease (AD). Two hundred and tenF18-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied. Methods: The proposed methodology is base...

This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease (AD). Two hundred and ten 18F-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied. The proposed methodology is based on the selection o...

This paper shows a machine learning approach based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) to compare the diagnostic accuracy on very early Alzheimer's Disease (AD) patients with 18F FDG and Pittsburg Compound B (PiB) PET imaging. The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset is used for testing, maki...

In this work, a procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before to proceed with the affine registration. The prepro...

Finding sensitive and appropriate technologies for early detection of the Alzheimer’s 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 vis...

Alzheimer’s disease (AD) 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 as Single-Photon Emission Computed Tomography (SPECT) is commonly used to guide the clinician’s diagnosis. The e...

This paper offers a computer-aided diagnosis (CAD) technique for early diagnosis of Alzheimer’s disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The SPECT database for different patients is analyzed by applying the Fisher discriminant ratio (FDR) and non-negative matrix factorization (NMF) for the se...

This work presents a computer-aided diagnosis technique for improving the accuracy of the diagnosis of the Alzheimer’s disease (AD). Some regions of the SPECT image discriminate more between healthy and AD patients than others, thus, it is important to design an automatic tool for selecting these regions. This work shows the performance of the Mann...

Single Photon Emission Computed Tomography (SPECT) images are commonly used by physicians to assist the diagnosis of several diseases such as Alzheimer’s disease (AD). The diagnosis process requires the visual evaluation of the image and usually entails time consuming and subjective steps. In this context, computer aided diagnosis (CAD) systems are...

In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer’s disease (AD). The proposed method is based on Association Rules (ARs) aiming to discover interesting associations between attributes contained in the database. The system uses firstly voxel-as-features (VAF) and Activation Estimation (A...

This letter presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of Alzheimer's disease (AD) based on non-negative matrix factorization (NMF) analysis applied to single photon emission computed tomography (SPECT) images. A baseline normalized SPECT database containing normalized data for both AD patients and healthy ref...