Molecular basis of Huntington's disease and brain imaging evidence

Plerou, Antonia; Bobori, Catherine; Vlamos, Panayiotis

doi:10.1109/isspit.2015.7394365

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Quantitative morphometric studies based on brain MRI, normally require separating the brain from extracranial or non-brain tissues through a process of isolation known as skull stripping [2]. Studies in discovery biomarkers for dementias like Alzheimer's Disease [3], [4] and Schizophrenia [5] [6], early detection of Huntington's Disease [7], [8], and tissue classification [9], among many others, process skull stripped brain MRI Skull stripping of brain MRI is a challenging task that continue being an active research problem, which has different solution approaches [10] [11] [12] [13] [14], all of them have their advantages and disadvantages. In this paper, we present a simple solution from a new approach based on saliency detection using dictionary learning and sparse coding, which operates indistinctly over T1 and T2 weighted axial brain MRI.…”

Section: Introductionmentioning

confidence: 99%

A New Approach on Skull Stripping of Brain MRI based on Saliency Detection using Dictionary Learning and Sparse Coding

Leal

Zurek²

2019

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In brain magnetic resonance images (brain MRI) analysis, for diagnosing certain brain conditions, it is necessary to quantify the brain tissue, which implies to separate the brain from extracranial or non-brain tissues through a process of isolation known as skull stripping. This is a non-trivial task since different types of tissues may have the same gray level, and during the separation process, some brain tissues could be removed. This paper presents a new solution approach for the skull stripping problem, based on saliency detection using dictionary learning and sparse coding, which can operate over T1 and T2 weighted axial brain MRI. Our method first subdivides the axial MRI into full overlapped patches and runs a dictionary learning over them for obtaining its sparse representation. Then, by analyzing the sparse coding matrix, we compute how many patches a dictionary atom affects to classify them as frequent or rare. Then, we calculate the saliency map of the axial MRI according to the composition of the image patches, i.e. an image patch is considered salient if it is mainly composed of frequent atoms, an atom is frequent whether it affects many patches. The non-salient pixels, corresponding to non-brain tissues, are eliminated from the MRI. Numerical results validate our method

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Section: Introductionmentioning

confidence: 99%

A New Approach on Skull Stripping of Brain MRI based on Saliency Detection using Dictionary Learning and Sparse Coding

Leal

Zurek²

2019

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“…The aim of such systems is to cooperate with a physician and to support him / her in decision-making processes, to prevent mistakes in decision making and to provide early warning [1]. We focus on CDSS for handwriting, since handwriting is non-invasive, inexpensive, and reflects the symptoms of many other disorders such as Parkinson's disease [2] [3], Alzheimer's disease [4], Huntington's disease [5], dysgraphia [6] [7] and many others. In response to increasing concerns about handwriting disorders, I focus specifically on dysgraphia, since it manifests itself in childhood.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Parameters From Dysgraphic Handwriting for CDSS Systems

Dankovicova¹,

Uchnar²

2019

AEI

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In this study we address the issue of the handwriting processing by extracting parameters from the written speech. The work applies machine learning method-the decision trees method which aims to recognize the impaired handwriting, particularly dysgraphia. 55 features (e.g. total time, pen movement, pressure, speed, acceleration) were extracted from each out of 80 handwriting samples while analyzing the performance of classifier for the dominant parameter-minimal speed, and without the dominant parameter as well. The experimental results of the classifier are compared to the results of the statistical test-Mann Whitney U-test as a complex and challenging endeavor to create an accurate classification.

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Diagnostic Evaluation of Huntington’s Disease Within the Frame of Bioinformatics

Bobori

2017

Advances in Experimental Medicine and Biology

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Huntington's disease is an autosomal dominant neurodegenerative disorder characterized by motor and cognitive impairment. At the early stages of the disease scientists have observed psychological changes and differences in patients' alpha, beta and theta powers. The early diagnosis of Huntington's disease is very important in order to administrate the correct medication to the patients and slow down the progress of the disease. This paper aims to suggest a protocol in which experiments could be based on, in an attempt to make an early diagnosis of the disease by taking into consideration the patients' electroencephalographic recordings during the early stages of the disease, as well as the methods of processing and classifying those EEG signals.

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Molecular basis of Huntington's disease and brain imaging evidence

Cited by 5 publications

References 20 publications

A New Approach on Skull Stripping of Brain MRI based on Saliency Detection using Dictionary Learning and Sparse Coding

A New Approach on Skull Stripping of Brain MRI based on Saliency Detection using Dictionary Learning and Sparse Coding

Extraction of Parameters From Dysgraphic Handwriting for CDSS Systems

Diagnostic Evaluation of Huntington’s Disease Within the Frame of Bioinformatics

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