This article introduces a variable selection and visualisation approach for medical imaging big data analysis based on Partial Least Squares, dubbed Picky Partial Least Squares. The method can handle very high-dimensional data and appears to be able to find relevant clusters of data points. It has been developed to deal in particular with large datasets. The method is validated experimentally on medical images from the ADNI (Alzheimer's Disease Neuroimaging Initiative). It is shown to perform better than standard PLS on the datasets and identifies relevant brain areas and SNPs as linked to Alzheimer's Disease. In particular the temporal lobes of the brain are highlighted by the algorithm, along with SNPs such as rs157580, which have previously been linked to Alzheimer's Disease. The method is also able to classify Alzheimer's patients from controls directly from the original high-dimensional imaging data, without any feature selection and dimension reduction. Unlike existing publications, the focus of this paper will be to select and visualise the image features that PPLS considers as related to Alzheimer's Disease.
Here we present a novel fusion technique for support vector machine (SVM) scores, obtained after a dimension reduction with a principal component analysis algorithm (PCA) for Gabor features applied to face verification. A total of 40 wavelets (5 frequencies, 8 orientations) have been convolved with public domain FRAV2D face database (109 subjects), with 4 frontal images with neutral expression per person for the SVM training and 4 different kinds of tests, each with 4 images per person, considering frontal views with neutral expression, gestures, occlusions and changes of illumination. Each set of wavelet-convolved images is considered in parallel or independently for the PCA and the SVM classification. A final fusion is performed taking into account all the SVM scores for the 40 wavelets. The proposed algorithm improves the Equal Error Rate for the occlusion experiment compared to a Downsampled Gabor PCA method and obtains similar EERs in the other experiments with fewer coefficients after the PCA dimension reduction stage.
Recently, nonnegative matrix factorization (NMF) with part-based representation has been widely used for appearance modeling in visual tracking. Unfortunately, not all the targets can be successfully decomposed as "parts" unless some rigorous conditions are satisfied. To avoid this problem, this paper introduces NMF's variants into the visual tracking framework in the view of data clustering for appearance modeling. First, an initial target appearance model based on NMF is proposed to describe the target's appearance with the incorporated local coordinate factorization constraint, orthogonality of the bases, and L 1,1 norm regularized sparse residual error constraint. Second, an inverse NMF model is proposed in which each learned base vector is regarded as a clustering center in a low-dimensional subspace. Potential target samples (from the foreground) will be clustered around base vectors, while the candidate samples (from the background) are very likely to spread irregularly over the entire clustering space. Such differences can be fully exploited by the inverse NMF model to produce more discriminative encoding vectors than the conventional NMF method. Furthermore, incremental updating model is introduced into the tracking framework for online updating the initial appearance model. Experiments on object tracking benchmark suggest that our tracker is able to achieve promising performance when compared with some state-of-the-art methods in deformation, occlusion, and other challenging situations.
This paper presents a novel vision-based interactive surface, referred to as the Virtual Touch Screen (VTS). The contents of the VTS can be displayed using a projector or on a Head Mounted Display (HMD), and the VTS is made touch-sensitive through unadorned visual articulated hand tracking. The VTS can potentially be used as, for example, an alternative to touch screens, an interface to mobile computing devices, interactive surface for information points, shop displays, video games, and as a sterile interface for use in hospitals and clean rooms. The capabilities of the VTS are demonstrated through a number of experiments, which involve interaction with various virtual interface elements, such as keypads, slider bars, control wheels, buttons, etc. Finally, a virtual drawing application demonstrates the use of the VTS to complete a task.
In this paper, we propose a computational framework for 3D volume reconstruction from 2D histological slices using registration algorithms in feature space. To improve the quality of reconstructed 3D volume, first, intensity variations in images are corrected by an intensity standardization process which maps image intensity scale to a standard scale where similar intensities correspond to similar tissues. Second, a subvolume approach is proposed for 3D reconstruction by dividing standardized slices into groups. Third, in order to improve the quality of the reconstruction process, an automatic best reference slice selection algorithm is developed based on an iterative assessment of image entropy and mean square error of the registration process. Finally, we demonstrate that the choice of the reference slice has a significant impact on registration quality and subsequent 3D reconstruction.
In this paper, we propose three novel and important methods for the registration of histological images for 3D reconstruction. First, possible intensity variations and nonstandardness in images are corrected by an intensity standardization process which maps the image scale into a standard scale where the similar intensities correspond to similar tissues meaning. Second, 2D histological images are mapped into a feature space where continuous variables are used as high confidence image features for accurate registration. Third, we propose an automatic best reference slice selection algorithm that improves reconstruction quality based on both image entropy and mean square error of the registration process. We demonstrate that the choice of reference slice has a significant impact on registration error, standardization, feature space and entropy information. After 2D histological slices are registered through an affine transformation with respect to an automatically chosen reference, the 3D volume is reconstructed by co-registering 2D slices elastically.
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