scholarly journals Multiple Sclerosis Detection in Multispectral Magnetic Resonance Images with Principal Components Analysis.

2008 ◽  
Author(s):  
Dirk-jan Kroon ◽  
Erik van Oort ◽  
Kees Slump
Keyword(s):  
Multiple Sclerosis ◽  
Feature Vector ◽  
Principal Component ◽  
Bias Field ◽  
Magnetic Resonance Images ◽  
Local Feature ◽  
Training Set ◽  
Edge Preserving ◽  
Vector Method ◽  
Components Analysis

This paper presents a local feature vector based method for automated Multiple Sclerosis (MS) lesion segmentation of multi spectral MRI data. Twenty datasets from MS patients with FLAIR, T1,T2, MD and FA data with expert annotations are available as training set from the MICCAI 2008 challenge on MS, and 24 test datasets. Our local feature vector method contains neighbourhood voxel intensities, histogram and MS probability atlas information. Principal Component Analysis(PCA) with log-likelihood ratio is used to classify each voxel. MRI suffers from intensity inhomogenities. We try to correct this ‘’bias field’’ with 3 methods: a genetic algorithm, edge preserving filtering and atlas based correction. A large observer variability exist between expert classifications, but the similarity scores between model and expert classifications are often lower. Our model gives the best classification results with raw data, because bias correction gives artifacts at the edges and flatten large MS lesions.

scholarly journals EEG-based Classification of Epileptic and Non-Epileptic Events using Multi-Array Decomposition

2016 ◽  
Vol 4 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Evangelia Pippa ◽  
Vasileios G. Kanas ◽  
Evangelia I. Zacharaki ◽  
Vasiliki Tsirka ◽  
Michael Koutroumanidis ◽  
...  
Keyword(s):  
Feature Vector ◽  
Matrix Representation ◽  
Vasovagal Syncope ◽  
Principal Component ◽  
Feature Space ◽  
Tucker Decomposition ◽  
Training Set ◽  
Spatiotemporal Model ◽  
Dimensional Domain

In this paper, the classification of epileptic and non-epileptic events from EEG is investigated based on temporal and spectral analysis and two different schemes for the formulation of the training set. Although matrix representation which treats features as concatenated vectors allows capturing dependencies across channels, it leads to significant increase of feature vector dimensionality and lacks a means of modeling dependencies between features. Thus, the authors compare the commonly used matrix representation with a tensor-based scheme. TUCKER decomposition is applied to learn the essence of original, high-dimensional domain of feature space. In contrast to other relevant studies, the authors extend the non-epileptic class to both psychogenic non-epileptic seizure and vasovagal syncope. The classification schemes were evaluated on EEG epochs from 11 subjects. The proposed tensor scheme achieved an accuracy of 97,7% which is better compared to the spatiotemporal model even after trying to improve the latter by dimensionality reduction through principal component analysis and feature selection.

scholarly journals Segmentation of MRI images to detect multiple sclerosis using non-parametric, non-uniform intensity normalization and support vector machine methods

2021 ◽  
Vol 13 (1) ◽  
pp. 68-74
Author(s):  
Mohammad Moghadasi ◽  
Gabor Fazekas
Keyword(s):  
Multiple Sclerosis ◽  
Support Vector Machine ◽  
Principal Component ◽  
Bias Field ◽  
Definitive Treatment ◽  
Support Vector ◽  
Average Accuracy ◽  
The Central Nervous System ◽  
Intensity Normalization ◽  
Mri Image

Multiple sclerosis (MS) is an inflammatory, chronic, persistent, and destructive disease of the central nervous system whose cause is not yet known but can most likely be the result of a series of unknown environmental factors reacting with sensitive genes. MRI is a method of neuroimaging studies that results in better image contrast in soft tissue. Due to the unknown cause of MS and the lack of definitive treatment, early diagnosis of this disease is important. MRI image segmentation is used to identify MS plaques. MRI images have an image error that is often called non-uniform light intensity. There are several ways to correct non-uniform images. One of these methods is Nonparametric Non-uniform intensity Normalization (N3). This method sharpens the histogram. The aim of this study is to reduce the effect of bias field on the MRI image using N3 algorithm and pixels of MRI images clustered by k-means algorithm. The dimensionality of the data is reduced by Principal Component Analysis (PCA) algorithm and then the segmentation is done by Support Vector Machine (SVM) algorithm. Results show that using the proposed system could diagnose multiple sclerosis with an average accuracy of 93.28%.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

scholarly journals An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 35 ◽  
Author(s):  
Hung-Cuong Trinh ◽  
Yung-Keun Kwon
Keyword(s):  
Scoring Function ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Filter Method ◽  
Feature Construction ◽  
Training Set ◽  
Robust Solution ◽  
Benchmark Datasets ◽  
Useful Life ◽  
Better Than

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

Lesion-to-ventricle distance and other risk factors for the persistence of newly formed black holes in relapsing–remitting multiple sclerosis

2013 ◽  
Vol 20 (3) ◽  
pp. 322-330 ◽  
Author(s):  
Athina Papadopoulou ◽  
Milena Menegola ◽  
Jens Kuhle ◽  
Sreeram V Ramagopalan ◽  
Marcus D’Souza ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Black Holes ◽  
Subventricular Zone ◽  
Magnetic Resonance Images ◽  
Lateral Ventricles ◽  
Relapsing Remitting ◽  
Specific Factors ◽  
One Year ◽  
Relapsing Remitting Multiple Sclerosis ◽  
The Relationship

Background: Progenitor cells from the subventricular zone (SVZ) of the lateral ventricles are assumed to contribute to remyelination and resolution of black holes (BHs) in multiple sclerosis (MS). This process may depend on the distance between the lesion and the SVZ. Objective: The objective of this paper is to investigate the relationship between lesion-to-ventricle (LV) distance and persistence of new BHs. Methods: We analysed the magnetic resonance images (MRIs) of 289 relapsing–remitting (RR) MS patients, obtained during a multi-centre, placebo-controlled phase II trial over one year. Results: Overall, 112/289 patients showed 367 new BHs at the beginning of the trial. Of these, 225 were located in 94/112 patients at the level of the lateral ventricles on axial MRIs and included in this analysis. In total, 86/225 (38%) BHs persisted at month 12. LV distance in persistent BHs (PBHs) was not longer than in transient BHs. In fact PBHs tended to be closer to the SVZ than transient BHs. A generalised linear mixed multivariate model adjusted for BHs clustered within a patient and including patient- as well as lesion-specific factors revealed size, ring contrast enhancement, and shorter LV distance as independent predictors for BH persistence. Conclusion: Location of BHs close to the lateral ventricles does not appear to favourably influence the resolution of new BHs in RRMS.

scholarly journals Multiple Sclerosis Of The Spinal Cord: Is Gadolinium Irreplaceable In Assessing Lesion Activity?

2013 ◽  
Vol 59 (3) ◽  
pp. 158-161
Author(s):  
Constantina Andrada Treabă ◽  
M Buruian ◽  
Rodica Bălașa ◽  
Maria Daniela Podeanu ◽  
I P Simu ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spinal Cord ◽  
Magnetic Resonance ◽  
Contrast Enhancement ◽  
Signs And Symptoms ◽  
Magnetic Resonance Images ◽  
Type I ◽  
Focal Lesions ◽  
Spinal Cord Lesions ◽  
Active Lesion

Abstract Purpose: To evaluate the relationship between the T2 patterns of spinal cord multiple sclerosis lesions and their contrast uptake. Material and method: We retrospectively reviewed the appearance of spinal cord lesions in 29 patients (with relapsing-remitting multiple sclerosis) who had signs and symptoms of myelopathy on neurologic examination and at least one active lesion visualized on magnetic resonance examinations performed between 2004 and 2011. We correlated the T2 patterns of lesions with contrast enhancement and calculated sensitivity and specificity in predicting gadolinium enhancement. Results: Only focal patterns consisting of a lesion’s center homogenously brighter than its periphery on T2-weighed images (type I) correlated significantly with the presence of contrast enhancement (p = 0.004). Sensitivity was 0.307 and specificity 0.929. In contrast, enhancement was not significantly related to uniformly hyperintense T2 focal lesions (type II) or diffuse (type III) pattern defined as poorly delineated areas of multiple small, confluent, subtle hyperintense T2 lesions (p > 0.5 for both). Conclusions: We believe that information about the activity of multiple sclerosis spinal cord lesions in patients with myelopathy may be extracted not only from contrast enhanced, but also from non-enhanced magnetic resonance images.

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