Optimization of distributions differences for classification

In this paper we introduce a new classification algorithm called Optimization of Distributions Differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another while the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the Quasi-Newton method. The choice of the transformation function is flexible and could be any continuous space function. We experiment with a linear and a non-linear transformation in this paper. We show that the algorithm can outperform 6 other state-of-the-art classification methods, namely naive Bayes, support vector machines, linear discriminant analysis, multi-layer perceptrons, decision trees, and k-nearest neighbors, in 12 standard classification datasets. Our results show that the method is less sensitive to the imbalanced number of instances comparing to these methods. We also show that ODD maintains its performance better than other classification methods in these datasets, hence, offers a better generalization ability

Radiological studies of fetal alcohol spectrum disorders in humans and animal models: an updated comprehensive review

Fetal Alcohol Spectrum Disorders encompass a wide range of birth defects in children born to mothers who consumed alcohol during pregnancy. Typical mental impairments in FASD include difficulties in life adaptation and learning and memory, deficits in attention, visuospatial skills, language and speech disabilities, mood disorders and motor disabilities. Multimodal imaging methods have enabled in vivo studies of the teratogenic effects of alcohol on the central nervous system, giving more insight into the FASD phenotype. This paper offers an up-to-date comprehensive review of radiological findings in the central nervous system in studies of prenatal alcohol exposure in both humans and translational animal models, including Magnetic Resonance Imaging, Computed Tomography, Positron Emission Tomography, Single Photon Emission Tomography and Ultrasonography. (C) 2017 Elsevier Inc. All rights reserved

A variant of differential evolution for discrete optimization problems requiring mutually distinct parameters

A large number of real world problems are formulated in terms of a set of discrete or integer variables for which an optimal set is obtained through appropriate optimization of a function. These problems are best represented using a set of discrete numbers over bounded or unbounded discrete spaces, in order to limit the search domain of the algorithm. In this work, Differential Evolution (DE) is used for the discrete prob- lem, where the search space is augmented to improve the performance of the technique. Although in principal DE is used to nd the optimal solution, the manner in which the space is stated and then searched is altered to improve the overall performance. Both unique and non-unique discrete sets of variables are investigated as control variables of the functions, and the algorithm for each is outlined accordingly. A number of established test functions are used to state the performance of the proposed DE discrete variable op- timization technique, when compared to real space DE optimization

Fast parallel image reconstruction using smacker for functional magnetic resonance imaging

SMACKER is a method of calculating sensitivity maps from k-space reconstruction coefficients using only a few lines of inner k-space. In this method the problem of sensitivities ending at object boundaries is eliminated, unlike in other established methods. The method allows for the rapid calculation of sensitivity profiles from images, and it is proposed here that the approach can be used in functional MRI to obtain reconstructed images in little time. Functional MRI relying on fast parallel reconstruction techniques naturally lends itself to a method that can generate and use sensitivity maps directly from images

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures

Mouse EEG spike detection based on the adapted continuous wavelet transform

Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording

The use of inverse phase Fourier image to accommodate intensity inhomogeneities in medical image registration

Medical image registration is generally faced with the confounding effect of spatially dependent intensity variations. This can be the case when images have been acquired using the same imaging modality, for example, in magnetic resonance imaging and also when using various histology and staining processes. We propose the application of an intensity-invariant dense feature extraction method through the use of phase Fourier transforms. The approach allows medical images containing intensity in homogeneities to be aligned and warped as part of a feature-based registration technique. Registration performance was evaluated on mono-modality and multi-modality data, namely magnetic resonance and histology images. Qualitative and quantitative validation was conducted with respect to two established image intensity correction methods

Haptic interaction of organs based on the spherical harmonic representation

Visual and haptic organ interactions in real time are essential in virtual reality–based medical simulations to provide training, early diagnosis, and improved treatment planning. This work presents a robust method of computing the deformation of an elastic object when external forces are applied. Point-based haptic interaction between the user of the haptic device and an object is outlined and is formulated as a deformation problem over the surface. The model is expressed in the spherical harmonic basis, from which both haptic device force feedback and object surface updates are calculated. Haptic and visual updates are performed in the same basis, in turn providing significant benefits of computing efficiency and multiresolution modeling. Partially, this is due to the fact that in the model the objects are represented as surfaces. A haptic device is used to probe different prostates that have been segmented and reconstructed from MR images to validate the findings. The calculated deformation and feedback force is further validated against the established finite element method