Intensity Nonuniformity Correction for Brain MR Images with Known Voxel Classes

Intensity nonuniformity in magnetic resonance (MR) images, represented by a smooth and slowly varying function, is a typical artifact that is a nuisance for many image processing methods. To eliminate the artifact, we have to estimate the nonuniformity as a smooth and slowly varying function and factor it out from the given data. We reformulate the problem as a problem of finding a unique smooth function in a particular set of piecewise smooth functions and propose a variational method for finding it. We deliver the main idea using a simple one-dimensional example first and provide a thorough analysis of the problem in a three-phase scenario in three dimensions whose application can be found in the brain MR images. Experiments with synthetic and real MR images and a comparison with a state-of-the-art method, N3, show our algorithm???s satisfactory performance in estimating the nonuniformity with and without noise. An automated procedure is also proposed for practical use.open

Continuous flexibility analysis of SARS-CoV-2 spike prefusion structures

Using a new consensus-based image-processing approach together with principal component analysis, the flexibility and conformational dynamics of the SARS-CoV-2 spike in the prefusion state have been analysed. These studies revealed concerted motions involving the receptor-binding domain (RBD), N-terminal domain, and subdomains 1 and 2 around the previously characterized 1-RBD-up state, which have been modeled as elastic deformations. It is shown that in this data set there are not well defined, stable spike conformations, but virtually a continuum of states. An ensemble map was obtained with minimum bias, from which the extremes of the change along the direction of maximal variance were modeled by flexible fitting. The results provide a warning of the potential image-processing classification instability of these complicated data sets, which has a direct impact on the interpretability of the results.The authors would like to acknowledge financial support from CSIC (PIE/COVID-19 No. 202020E079), the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Science and Innovation through projects SEV 2017-0712, FPU-2015/264 and PID2019-104757RB-I00/AEI/ FEDER, the Instituto de Salud Carlos III [PT17/0009/0010 (ISCIII-SGEFI/ERDF)], and the European Union and Horizon 2020 through grants INSTRUCT–ULTRA (INFRADEV-03-2016-2017, Proposal 731005), EOSC Life (INFRAEOSC-04-2018, Proposal 824087), HighResCells (ERC-2018-SyG, Proposal 810057), IMpaCT (WIDESPREAD- 03-2018, Proposal 857203), CORBEL (INFRADEV-1-2014-1, Proposal 654248) and EOSC–Synergy (EINFRA-EOSC-5, Proposal 857647). HDT and BF were supported by NIH grant GM125769 and JSM was supported by NIH grant R01-AI12752

Estimation of repetition rate from signal and texture features

This thesis develops relevant definitions and a theoretical basis for estimating the repetition rate of a random repetitive signal. The repetition rate is estimated by looking for repetition amongst local features of the signals. These features have to satisfy a uniqueness condition, and we have shown that the derivatives of a signal constitute a set of such features. The estimator has been shown to be asymptotically unbiased. The estimation algorithm can not only be tuned to the waveshape information of the signal (by a proper choice of features), but also to the extent of non-stationarity expected in the input signal class. A set of features has been obtained for applying this algorithm to repetitive textured images and voiced speech signals. Vith these features, it has been possible to extract the repetition rate in both the above classes of signals. In the case of voiced speech this rate corresponds to its pitch

A theory of photometric stereo for a general class of reflectance maps

Photometric stereo is an image processing technique for 2$1\over 2$ dimensional surface reconstruction from local shading. The classical theory of photometric stereo has been developed only for surfaces that reflect in a Lambertian plus specular manner. However, there is plenty of experimental evidence that most real-world surfaces are not Lambertian plus specular. This thesis develops the theory of photometric stereo for non-Lambertian surfaces. First, based on the physics of reflection and scattering, a general class of reflectance maps is proposed. This class is shown to model real world data more accurately than the Lambertian model. Then, the normalized photometric stereo equation using these reflectance maps is analyzed and conditions for a globally unique solution for the equation are obtained. Furthermore, the un-normalized photometric stereo equation is studied and conditions for getting a globally unique solution using only three light sources are identified. The problem of jointly estimating the reflectance map and the surface normal is proposed and shown to be ill-posed. A regularized solution to the problem is demonstrated. Finally, it is shown that extra light sources are needed to obtain a complete reconstruction of the surface, and the number of new light sources needed to achieve this is identified

Efficient Shape-Based Retrieval In Medical Image Databases Using KD-Trees

ientation sensitive. Second, some non-rigid slack is required while comparing organs of different subjects to compensate for normal variation in organ development. However, the slack cannot be too large or it will compensate for large changes of shape instead. The measure E(C ex ; C) is created by finding a function OE : C ex ! C such that for every point x 2 C ex the tangent vector T ex (x) is as similar as possible to the tangent vector T (OE(x)) at the point OE(x) 2 C. Tangent 2 GLYNN. P. ROBINSON AND HEMANT. D. TAGARE vectors make the shape comparison orientation sensitive. The function OE mimics a "non-rigid slack". The amount of slack is constrained. Once

Shape-based retrieval in medical image databases using KD-trees.

The capacity to retrieve images containing objects with shapes similar to a query shape is desirable in medical image databases. We propose a similarity measure and an indexing mechanism for non-rigid comparison of shape which adds this capability to image databases. The (dis-) similarity measure is based on the observations that, (1) the geometry of the same organ in different subjects is not related by a strictly rigid transformation, and (2) the orientation of the organ pays a key role in comparing shape. We propose a similarity measure that computes a non-rigid mapping between curves and uses this mapping to compare oriented shape. We also show how KD-trees can index curves so that retrieval with our similarity measure is efficient. Experiments with real-world data from a database of magnetic resonance images are provided. 1 Introduction Geometric properties of anatomical organs are hard to describe precisely in words; it is much easier to use visual examples. Consequently, intera..

A New Framework of Multiphase Segmentation and Its Application to Partial Volume Segmentation

We proposed a novel framework of multiphase segmentation based on stochastic theory and phase transition theory. Our main contribution lies in the introduction of a constructed function so that its composition with phase function forms membership functions. In this way, it saves memory space and also avoids the general simplex constraint problem for soft segmentations. The framework is then applied to partial volume segmentation. Although the partial volume segmentation in this paper is focused on brain MR image, the proposed framework can be applied to any segmentation containing partial volume caused by limited resolution and overlapping

Visual Place Recognition for Autonomous Robots

The problem of place recognition is central to robot map learning. A robot needs to be able to recognize when it has returned to a previously visited place, or at least to be able to estimate the likelihood that it has been at a place before. Our approach is to compare images taken at two places, using a stochastic model of changes due to shift, zoom, and occlusion to predict the probability that one of them could be a perturbation of the other. We have performed experiments to gather the value of a Ø 2 statistic applied to image matches from a variety of indoor locations. Image pairs gathered from nearby locations generate low Ø 2 values, and images gathered from different locations generate high values. The rate of false positive and false negative matches is low. I. Introduction This paper presents a new visual place recognition algorithm. The algorithm accepts two images taken from two poses, and tests whether they the two poses are (probably) close to each other. The success..