Ekstraksi Objek Pada Citra Radar FM-CW Dengan Metode DBSCAN

Makalah ini membahas rancang bangun dan implementasi ekstraksi objek pada radar FM-CW untuk mengatasi permasalahan kualitas citra yang ditangkap oleh radar. Teknik clustering density based spatial clustering of applications with noise (DBSCAN) digunakan untuk mengekstraksi objek dari data input. Hasil dari penelitian ini adalah rancang bangun ekstrasi objek dengan nilai minPts sebesar 4 dan nilai eps sebesar 4 sebagai parameter input untuk DBSCAN. Hasil dari rancang bangun ekstraksi objek adalah titik-titik data hasil ekstraksi objek yang lebih sederhana yang mampu mengatasi permasalahan kualitas citra yang ditangkap oleh radar. Selain itu, titik-titik data yang dihasilkan juga memiliki kualitas data yang lebih baik karena teknik clustering DBSCAN memiliki kemampuan untuk memisahkan noise dari data input

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease

AbstractMagnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N=77) from the prospective registry on dementia study and controls (N=173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification

Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective

Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinician

Feature selection methods for predicting pre-clinical stage in Alzheirmer's Disease

Project in collaboration with the Pasqual Maragall Foundation and the Hospital Clinic from BarcelonaAlzheimer's disease is still an incurable disease. Nevertheless, some of its biomarkers suffer changes in the early stages of the disease, long before clinical symptoms appear. In order to determine how biomarkers obtained from magnetic resonance (MRI) techniques affect the disease's evolution, machine learning techniques have been used to design and implement a classification system so as to predict the stages in which several patients belong. One of the main objectives of this project is reducing the number of data to manage, since MRI provide a large volume of data for each patient. As a result, we will focus on the stage of reduction and extraction of characteristics of the classifier which may be relevant for the mentioned problem. We will carry out an exhaustive analysis of different methods of selection of features to apply to biomedical data related to Alzheimer's disease. Results obtained will also be applicable to other fields. Finally, we will assess these methods with a multimodal data base provided by the collaboration agreement with Pasqual Maragall Foundation (FPM).La enfermedad del Alzheimer es aún una enfermedad incurable. Sin embargo, algunos de sus biomarcadores sufren cambios durante las primeras etapas de la enfermedad, mucho antes de presentar síntomas clínicos. Para determinar cómo estos afectan a la evolución de la enfermedad los biomarcadores obtenidos a partir de técnicas de resonancia magnética (MRI), se han utilizado técnicas de machine learning para diseñar e implementar un sistema de clasificación con el fin de predecir las etapas en las que se encuentran distintos pacientes. Uno de los principales objetivos de este proyecto es reducir el número de datos a tratar, ya que las MRI proporcionan un gran volumen de datos de cada paciente. En consecuencia, nos centraremos en la etapa de reducción y extracción de características del clasificador que pueden ser relevantes para el problema mencionado. Realizaremos un análisis exhaustivo de distintos métodos de selección de características para aplicarlos a datos biomédicos relacionados con la enfermedad del Alzheimer. Los resultados obtenidos también podrán aplicarse en otros campos. Finalmente, evaluaremos los métodos con una base de datos multimodal proporcionada por el convenio de colaboración con la Fundació Pasqual Maragall (FPM).La malaltia de l'Alzheimer és encara una malaltia incurable. Tanmateix, alguns dels seus biomarcadors es pateixen canvis durant les primeres etapes de la malaltia, molt abans de presentar símptomes clínics. Per a determinar com afecten a l'evolució de la malaltia els biomarcadors obtinguts a partir de tècniques de ressonància magnètica (MRI), s'han utilitzat tècniques de machine learning per a dissenyar i implementar un sistema de classificació per a predir les etapes en què es troben diversos pacients. Un dels principals objectius d'aquest projecte és reduir el nombre de dades a tractar, ja que les MRI proporcionen un gran volum de dades de cada pacient. En conseqüència, ens centrarem en l'etapa de reducció i extracció de característiques del classificador que poden ser rellevants per al problema esmentat. Realitzarem una anàlisi exhaustiva de diferents mètodes de selecció de característiques per a aplicar-los a dades biomèdiques relacionades amb la malaltia de l'Alzheimer. Els resultats obtinguts també podran aplicar-se en altres camps. Finalment, avaluarem els mètodes amb una base de dades multimodal proporcionada pel conveni de col·laboració amb la Fundació Pasqual Maragall (FPM)

Multivariate decoding of brain images using ordinal regression.

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection

Neuroimaging of dementia in 2013: what radiologists need to know

The structural and functional neuroimaging of dementia have substantially evolved over the last few years. The most common forms of dementia, Alzheimer disease (AD), Lewy body dementia (LBD) and fronto-temporal lobar degeneration (FTLD), have distinct patterns of cortical atrophy and hypometabolism that evolve over time, as reviewed in the first part of this article. The second part discusses unspecific white matter alterations on T2-weighted and fluid-attenuated inversion recovery (FLAIR) images as well as cerebral microbleeds, which often occur during normal aging and may affect cognition. The third part summarises molecular neuroimaging biomarkers recently developed to visualise amyloid deposits, tau protein deposits and neurotransmitter systems. The fourth section reviews the utility of advanced image analysis techniques as predictive biomarkers of cognitive decline in individuals with early symptoms compatible with mild cognitive impairment (MCI). As only about half of MCI cases will progress to clinically overt dementia, whereas the other half remain stable or might even improve, the discrimination of stable versus progressive MCI is of paramount importance for both individual patient treatment and patient selection for clinical trials. The fifth and final part discusses the inter-individual variation in the neurocognitive reserve, which is a potential constraint for all proposed methods. Key Points • Many forms of dementia have spatial atrophy patterns detectable on neuroimaging. • Early treatment of dementia is beneficial, indicating the need for early diagnosis. • Advanced image analysis techniques detect subtle anomalies invisible on radiological evaluation. • Inter-individual variation explains variable cognitive impairment despite the same degree of atroph

Supervised machine learning in psychiatry:towards application in clinical practice

In recent years, the field of machine learning (often named with the more general term artificial intelligence) has literally exploded and its application has been proposed in basically all fields, including psychiatry and mental health. This has been motivated by the promise of using machine learning to develop new clinical tools that could help perform personalized predictions and recommendations, ultimately improving the results achievable in the psychiatric clinical practice that still faces only a limited success in the fight against mental diseases. However, despite this huge interest, there is still a substantial lack of tools in psychiatry that are based on machine learning algorithms. Massimiliano Grassi, in his Ph.D. thesis, investigates the challenges of translating machine learning algorithms into clinical practice and proposes innovative solutions to these challenges. The thesis presents the development and validation of new algorithms for the prediction of the onset of Alzheimer’s disease, the remission of obsessive-compulsive disorder, and the automatization of sleep staging in polysomnography, a method to diagnose sleep disorders. The results from these studies demonstrate that the use of machine learning in psychiatric clinical practice is not just a promise, and it is possible to develop machine learning algorithms that achieve clinically relevant performance even if based solely on information that can be easily accessible in the daily clinical routine

Development of Gaussian Learning Algorithms for Early Detection of Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer\u27s Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot be reversed or stopped. This research aims to develop Gaussian learning algorithms, natural language processing (NLP) techniques, and mathematical models to effectively delineate the MCI participants from the cognitively normal (CN) group, and identify the most significant brain regions and patterns of changes associated with the progression of AD. The focus will be placed on the earliest manifestations of the disease (early MCI or EMCI) to plan for effective curative/therapeutic interventions and protocols. Multiple modalities of biomarkers have been found to be significantly sensitive in assessing the progression of AD. In this work, several novel multimodal classification frameworks based on proposed Gaussian Learning algorithms are created and applied to neuroimaging data. Classification based on the combination of structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers is seen as the most reliable approach for high-accuracy classification. Additionally, changes in linguistic complexity may provide complementary information for the diagnosis and prognosis of AD. For this research endeavor, an NLP-oriented neuropsychological assessment is developed to automatically analyze the distinguishing characteristics of text data in MCI group versus those in CN group. Early findings suggest significant linguistic differences between CN and MCI subjects in terms of word usage, vocabulary, recall, fragmented sentences. In summary, the results obtained indicate a high potential of the neuroimaging-based classification and NLP-oriented assessment to be utilized as a practically computer aided diagnosis system for classification and prediction of AD and its prodromal stages. Future work will ultimately focus on early signs of AD that could help in the planning of curative and therapeutic intervention to slow the progression of the disease

Fractal tool; Calculating 3D fractal dimension of brain regions for a Dementia classification problem

Η άνοια είναι ένα σύνδρομο που είναι κοινό στους ηλικιωμένους και ο ρυθμός εμφάνισής του αυξάνεται. Η πλειονότητα των μελετών επικεντρώνεται στην εύρεση βιοδεικτών για διάγνωση, ενώ η πρόληψη και η παρακολούθηση της ανάπτυξης είναι ακόμη ένα αδύνατο έργο. Σήμερα, η έρευνα για την άνοια περιορίζεται στη νευροαπεικόνιση, καθώς είναι μια μη επεμβατική τεχνολογία. Υπάρχει μια πληθώρα νευροαπεικονιστικών εργαλείων τα οποία βελτιστοποιούν την απεικόνιση μιας εισαγόμενης εικόνας μέσω της επεξεργασίας εικόνας ή ακόμη και συνεισφέρουν στη λήψη ιατρικών αποφάσεων μέσω της ανάλυσης της εικόνας. Ωστόσο, η ατροφία του εγκεφάλου στην άνοια δεν έχει ακόμη χαρακτηριστεί σωστά. Η νευροαπεικόνιση στοχεύει κυρίως στην παρακολούθηση της μείωσης του όγκου του εγκεφάλου και λιγότερο σε άλλες δομικές υφές. Τέτοια χαρακτηριστικά είναι η μορφοκλασματική διάσταση και η ύπαρξη κενών στις εγκαφαλικές δομές (lacunarity). Μερικά εργαλεία νευροαπεικόνισης υπολογίζουν τη διάσταση του φράκταλ και του lacunarity για ολόκληρο τον όγκο του εγκεφάλου. Ωστόσο, η λειτουργικότητά τους δεν περιλαμβάνει αυτοματοποιημένη εκτίμηση πολλαπλών εικόνων και, συνεπώς, δημιουργία συνόλων δεδομένων (datasets). Υπολογίζουν απλώς αυτά τα χαρακτηριστικά για συγκεκριμένες δομές, ενώ δεν εκτελούν τα απαραίτητα βήματα επεξεργασίας εικόνας. Δεδομένου ότι τα περισσότερα από αυτά τα εργαλεία εξειδικεύονται σε συγκεκριμένες εργασίες, δεν υπάρχει μια ολιστική μέθοδος που εισάγει πολλαπλά δεδομένα απεικόνισης και εξάγει μετρήσεις για τη μορφοκλασματική διάσταση και το lacunarity. Αυτή η μελέτη παρουσιάζει ένα εργαλείο γενικού σκοπού για την αυτοματοποιημένη επεξεργασία εικόνας, την τμηματοποίηση εικόνας, την εκτίμηση της μορφοκλασματική διάσταση, του lacunarity και άλλων υφών που προέρχονται από τον υπολογισμό της μορφοκλασματικής διάστασης. Τα εξαγόμενα αρχεία είναι σύνολα δεδομένων με τέτοιες μετρήσεις. Έπειτα γίνεται μια ταξινόμηση υγειών και ατόμων με άνοια η οποία επιβεβαιώνει τη χρησιμότητα του λογισμικού. Παρόλο που υπήρχαν περιορισμοί στην απόκτηση δεδομένων, πραγματοποιήθηκε αποτελεσματική ταξινόμηση με μηχανές διανυσματικής υποστήριξης. Για αρκετές περιοχές του εγκεφάλου, η ακρίβεια Fbeta score κυμαινόταν μεταξύ 95% και 100% υπερισχύοντας όλων των άλλων μεθόδων. Ωστόσο, η διάγνωση της άνοιας απαιτεί ένα μοντέλο που διαχωρίζει αποτελεσματικά τις περιοχές του εγκεφάλου για όλες τις κλάσεις. Σε αυτή τη διατριβή, εκπαιδεύτηκαν δύο τέτοια μοντέλα, ένα κάθε ομάδα. Παρ 'όλα αυτά, τα τελικά αποτελέσματα αναδεικνύουν την αναγκαιότητα των μορφοκλασματικών ιδιοτήτων ως εργαλείο για τη ταξινόμηση των σταδίων της άνοιας και τη παρακολούθηση της ανάπτυξης της άνοιας. Επίσης, η χρήση του λογισμικού μπορεί να επεκταθεί σε οποιοδήποτε πρόβλημα δομικής νευροαπεικόνισης όπως η ανίχνευση καρκίνουDementia is a syndrome that is common amongst the elder adults and its occurrence rate is on the rise. The majority of the studies are focused on finding biomarkers for diagnosis, while prevention and monitoring of the development is yet an impossible task. Nowadays, research on dementia is limited to neuroimaging as it is a non-invasive technology. There is a plethora of neuroimaging tools which optimize the virtualization of an imported image through image processing or even contribute medical decision making through image analysis. Still, brain atrophy in Dementia is yet to be characterized properly. Neuroimaging mainly aims on volume decline of brain volume and less on other structural textures. Such features are fractal dimension and lacunarity. Some neuroimaging tools calculate fractal dimension and lacunarity for whole brain volume. However, their functionality does not include automated estimation of multiple images and thus creation of datasets. They just compute these features for given structures while not performing the necessary image processing steps. As most of these tools are specialized in specific tasks, there is not a holistic method that inputs multiple imaging data and exports measurements for fractal dimension, lacunarity. This study presents a general purpose tool for automated image processing, image segmentation, estimation of fractal dimension, lacunarity and other textures that are derived from the calculation of fractal dimension. The exported files are datasets with such measurements. Finally, a classification between healthy and dementia subjects underlines the utility of the software. Even though there were limitations to data acquirement, efficient classification with SVM models has been performed. For several brain regions, the ranging Fbeta score accuracy was 97% - 100 % outperforming all other methods. However, diagnosis of Dementia requires a unique prior model which efficiently segment brain regions for any given class. In this thesis, two models were trained, one for each group. Nevertheless, the final results reveal the necessity of fractal properties as a tool for Dementia classification and monitoring of the Dementia development. Also, the utility of the software can be extended to any structural neuroimaging problem such as detection of cancer

Classification techniques for Alzheimer’s disease early diagnosis

Proyecto en colaboración con el Hospital Clínic y la Fundación Pascual Maragall.[ANGLÈS] Alzheimer’s disease currently affects more than 36 million people in the world. A patient’s brain suffers changes during the earliest stages of the disease and long before showing any clinical symptoms. For that reason, researchers focus their efforts towards defining which changes occur and where do they take place, with the goal of detecting indicators to predict the development of the disease. Specifically, the entity Fundación Pascual Maragall para la investigación contra el Alzheimer studies the processes of the brain all along the disease’s stages using images obtained through different MRI techniques. The huge volume of data generated in this kind of investigation is a big obstacle to carry out analysis and extracting conclusions. The aim of this thesis is making this process easier by using data mining techniques. The goal is to develop a basic classification system to distinguish in which stage of the disease a patient is in, using data extracted from cerebral images. This system must form the basis for a future data mining system that satisfies the necessities of the Fundación Pascual Maragall researchers. In addition to the classification system, this project focuses on distinguishing which is the most relevant data in the classification and on optimizing the classification in the pre-clinical stage of the disease.[CASTELLÀ] La enfermedad de Alzheimer afecta actualmente a más de 36 millones de personas en el mundo. El cerebro de un paciente sufre cambios durante las etapas más tempranas de la enfermedad y mucho antes de presentar síntomas clínicos. Por esta razón, los investigadores se centran sus esfuerzos en determinar qué cambios se producen y dónde, con el objetivo de detectar indicadores para predecir el desarrollo de la enfermedad. En concreto, la Fundación Pascual Maragall para la investigación contra el Alzheimer estudia los procesos del cerebro a lo largo de la enfermedad a través de imágenes obtenidas mediante distintas técnicas de resonancia magnética. El gran volumen de datos que genera este tipo de investigación es un gran obstáculo para la realización de análisis y extracción de conclusiones. El objetivo de este proyecto es precisamente facilitar este proceso a través de técnicas de minería de datos. La meta es el desarrollo de un sistema básico de clasificación que permita discernir en qué etapa de la enfermedad de Alzheimer se encuentra un paciente a partir de datos extraídos de diferentes tipos de imágenes cerebrales. Este sistema debe constituir la base de un futuro sistema de minería de datos más complejo capaz de satisfacer las necesidades del grupo de investigación de la Fundación Pascual Maragall. Además de la implementación del sistema de clasificación, el proyecto se centra en la distinción de los datos más relevantes para la clasificación y en la optimización de la clasificación en la etapa pre-clínica del Alzheimer.[CATALÀ] La malaltia d’Alzheimer afecta actualment a més de 36 milions de persones al món. El cervell de un pacient pateix canvis durant les primeres etapes de la malaltia y molt abans de presentar símptomes clínics. Per aquesta raó, els investigadors centren els seus esforços en determinar quins canvis es produeixen i on, amb l’objectiu de detectar indicadors per predir el desenvolupament de la malaltia. En concret, la Fundació Pascual Maragall per a la investigació contra l’Alzheimer estudia els processos del cervell al llarg de la malaltia a través de imatges obtingudes a partir de diferents tècniques de ressonància magnètica. El gran volum de dades que genera aquest tipus de investigació és un gran obstacle per la realització d’anàlisis y extracció de conclusions. L’objectiu d’aquest projecte es precisament facilitar aquest procés a través de tècniques mineria de dades. La meta és el desenvolupament d’un sistema bàsic de classificació que permeti discernir en quina etapa de la malaltia d’Alzheimer es troba un pacient a partir de dades de diferents tipus d’imatges cerebrals. Aquets sistema ha de constituir la base d’un futur sistema de mineria de dades més complex capaç de satisfer les necessitats del grup d’investigació de la Fundació Pascual Maragall. A més de la implementació del sistema de classificació, el projecte es centra en la distinció de les dades més rellevants per a la classificació y en la optimització de la classificació en l’etapa pre-clínica de l’Alzheimer