Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall Cox–Stuart tests detecting trends; Pettitt, Buishand range, U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks additive season trend (BFAST), hierarchical divisive (E.divisive) change-points. In this paper, we describe a simulation study based on artificial,...

We propose a novel and accurate method based on ultrasound RF time series analysis an extended version of support vector machine classification for generating probabilistic cancer maps that can augment images prostate enhance the biopsy process. To form series, we record sequential echoes backscattered from tissue while imaging probe are stationary in position. show acquired agar-gelatin-based mimicking phantoms, with difference only size cell-mimicking microscopic glass beads,...

Abstract Within the applications of spatial point processes, it is increasingly becoming common that events are labelled by marks, prompting an exploration beyond distribution incorporating marks in undertaken analysis. In this paper, we first consider marked processes $$\mathbb {R}^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:math> , where either integer-valued, real-valued, or...

Voronoi estimators are non-parametric and adaptive of the intensity a point process. The estimate at given location is equal to reciprocal size Voronoi/Dirichlet cell containing that location. Their major drawback they tend paradoxically under-smooth data in regions where density observed pattern high, over-smooth low. To remedy this behaviour, we propose apply an additional smoothing operation estimator, based on resampling by independent random thinning. Through simulation study show our...

Summary We propose a computationally efficient and statistically principled method for kernel smoothing of point pattern data on linear network. The locations, the network itself, are convolved with two‐dimensional then combined into an intensity function This can be computed rapidly using fast Fourier transform, even large networks bandwidths, is robust against errors in geometry. estimator consistent, its statistical efficiency only slightly suboptimal. discuss bias, variance, asymptotics,...

Parasitic worms are significant causes of human and livestock disease. The battle against infections caused by parasitic involves the exploration numerous potential drug candidates. One approach in screening for new candidates is using natural product extracts on nematode C. elegans as a model organism. A critical step this process examination microscopy images after exposure to extracts. Automatic image classification accelerates analysis compared purely visual identification an expert. We...

We propose an extension of Diggle's nonparametric edge-corrected kernel-based intensity estimator to the case events coming from inhomogenous point pattern on a linear network. analyze its statistical properties, showing that it is unbiased first-order intensity; we also provide expression for variance, and comment appropriate bandwidth selection. Our compared with current existing equal-split discontinuous kernel density in terms mean integrated squared error (MISE). then use our two real...

We present several characteristics for spatio-temporal point patterns when the spatial locations are restricted to a linear network. A nonparametric kernel-based intensity estimator is proposed highlight concentration of events within network and time, either jointly or separately. also provide second-order on networks such as K-function pair correlation function analyze type interaction between points. They independent network's geometry have known values Poisson processes. Finally, we...

Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through approaches. The former discards any inherent dependencies between different and the latter may suffer from domination/masking among of distinct marginals. As a remedy, we propose an approach which groups with similar temporal behaviors, then performs group-wise change-point detection. Our based on hierarchical clustering using...

We propose novel second/higher-order summary statistics for inhomogeneous spatio-temporal point processes when the spatial locations are limited to a linear network. More specifically, letting distance between events be measured by regular metric, appropriate forms of K- and J-functions introduced, their theoretical relationships studied. The our proposed investigated under homogeneity, Poissonness, independent thinning. Moreover, non-parametric estimators derived, facilitating use study...

Background: There is an increasing consensus that ecosystem processes are governed by functional identity and trait variation rather than species richness. Despite its importance, the relative effect of relevant traits for carbon storage has remained mostly untested in different bioclimatic regions.Aims: In this study, components diversity such as community-weighted means values (CWM), (Rao's quadratic diversity), richness (FRi), evenness (FEv) divergence (FDiv) were used to associate...

In this article, we describe a system for detecting dominant prostate tumors, based on combination of features extracted from novel multi-parametric quantitative ultrasound elastography technique. The performance the was validated data-set acquired n = 10 patients undergoing radical prostatectomy. Multi-frequency steady-state mechanical excitations were applied to each patient's through perineum and tissue displacements captured by transrectal system. 3D volumetric data including absolute...

Abstract Motivated by the general ability of cross-validation to reduce overfitting and mean square error, we develop a cross-validation-based statistical theory for point processes. It is based on combination two novel concepts processes: prediction errors. Our approach uses thinning split process/pattern into pairs training validation sets, while our errors measure discrepancy between The new approach, which may be used model different distributional characteristics, exploits how well...

In this paper, we demonstrate that a set of six features extracted from the discrete Fourier transform ultrasound radio-frequency (RF) time series can be used to detect prostate cancer with high sensitivity and specificity. Ultrasound RF refer echoes received one spatial location tissue while imaging probe are fixed in position. Our previous investigations have shown at least feature, fractal dimension, these signals demonstrates strong correlation microstructure. current new represent...

Purpose: Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method detecting using vibroelastography (VE) system developed in our group tissue classification approach based on texture analysis VE images. Methods: applies wide-band mechanical vibrations to the tissue. Here, report use this detection show that images characterized by first second order statistics pixel intensities form promising set features typing detect...

We propose a novel fiducial-free approach for the registration of C-arm fluoroscopy to 3-D ultrasound images prostate brachytherapy implants enable dosimetry. The involves reliable detection subset radioactive seeds from ultrasound, and use needle tracks in both registration. Seed is achieved through template matching radio frequency signals, followed by thresholding spatial filtering. resulting registered complete reconstruction implant multiple views. To compensate deformation caused...

Participatory sensing combines the powerful capabilities of current mobile devices with mobility and intelligence human beings, as such has to potential collect various types information at a high spatial temporal resolution. Success, however, entirely relies on willingness motivation users carry out tasks, thus it is essential incentivize users&rsquo; active participation. In this article, we first present an open, generic participatory framework (Citizense) which aims make more accessible,...