Acute pulmonary embolism pretest probability estimation by d-dimer test, our modified, new ECG score and clinical prediction rules.

Objectives: We investigated whether a sufficiently sensitive D-dimer test could exclude acute pulmonary embolism (acPE) as a stand-alone diagnostic test and compared our previously published, modified ECG score with the Wells and Geneva scores in the estimation of acPE pretest probability. Methods: We retrospectively evaluated 345 patients who underwent chest CT angiography (CTA) for the suspicion of acPE. The pretest probability of acPE was assessed in 120 D-dimer negative [DD (-)] and 225 D-dimer positive [DD (+)] patients. Results: Chest CTA verified acPE in 57/345 (16.5 %) patients and in 1/120 (0.8 %) DD (-) patient. In DD (-) patients the test accuracy (TA) and specificity (SP) of the ECG score (98 %, 99 %) were better than those of the Wells score (92.5 %, 92.4 %) (p = 0.063 and p < 0.05 respectively) and the Geneva score (76.7 %, 76.5 %) (p < 0.001 for both), the Wells score TA and SP were greater than those of the Geneva score (p < 0.001 for both). In DD (+) patients the SPs, TAs and positive predictive values (PPV) of the ECG score (94 %, 78.6 %, 69 %) and the Wells score (91.8 %, 75.1 %, 48 %) were greater than those of the Geneva score (71.3 %, 64.9 %, 38.2 %) (p < 0.001 for both SP and TA respectively, and p < 0.001 for PPV of the ECG score vs. the Geneva score and p < 0.05 for PPV of the Wells score vs. Geneva score), their sensitivities (SE) (36.4 %, 23.6 %) were less than that of the Geneva score (47.5 %) (p < 0.05 and p < 0.001 respectively). The ECG score's TA in a trend, its SE and PPV were significantly (p < 0.01 and p < 0.001) better than those of the Wells score. Conclusion: In contrast to the current guidelines, a stand-alone high sensitivity DD (-) test, without prediction rules, could reliably exclude acPE. Our ECG score slightly outperformed the Wells score, the ECG score and Wells score far outperformed the Geneva score in the estimation of acPE pretest probability. An acPE diagnosis with the ECG score, in addition to the supportive diagnosis with the clinical prediction rules, may further increase the chance of true DD positivity.

Steer'n'Detect: fast 2D template detection with accurate orientation estimation.

MOTIVATION: Rotated template matching is an efficient and versatile algorithm to analyze microscopy images, as it automates the detection of stereotypical structures, such as organelles that can appear at any orientation. Its performance however quickly degrades in noisy image data. RESULTS: We introduce Steer'n'Detect, an ImageJ plugin implementing a recently published algorithm to detect patterns of interest at any orientation with high accuracy from a single template in 2D images. Steer'n'Detect provides a faster and more robust substitute to template matching. By adapting to the statistics of the image background, it guarantees accurate results even in the presence of noise. The plugin comes with an intuitive user interface facilitating results analysis and further post-processing. AVAILABILITY AND IMPLEMENTATION: https://github.com/Biomedical-Imaging-Group/Steer-n-Detect. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Principled Design and Implementation of Steerable Detectors.

We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown positions and orientations that we aim at retrieving. We propose a continuous-domain additive image model, where the analyzed image is the sum of the patterns to localize and a background with self-similar isotropic power-spectrum. We are then able to compute the optimal filter fulfilling the SNR criterion based on one single template and background pair: it strongly responds to the template while being optimally decoupled from the background model. In addition, we constrain our filter to be steerable, which allows for a fast template detection together with orientation estimation. In practice, the implementation requires to discretize a continuous-domain formulation on polar grids, which is performed using quadratic radial B-splines. We demonstrate the practical usefulness of our method on a variety of template approximation and pattern detection experiments. We show that the detection performance drastically improves when we exploit the statistics of the background via its power-spectrum decay, which we refer to as spectral-shaping. The proposed scheme outperforms state-of-the-art steerable methods by up to 50% of absolute detection performance.

Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification.

We present texture operators encoding class-specific local organizations of image directions (LOIDs) in a rotation-invariant fashion. The LOIDs are key for visual understanding, and are at the origin of the success of the popular approaches, such as local binary patterns (LBPs) and the scale-invariant feature transform (SIFT). Whereas, LBPs and SIFT yield hand-crafted image representations, we propose to learn data-specific representations of the LOIDs in a rotation-invariant fashion. The image operators are based on steerable circular harmonic wavelets (CHWs), offering a rich and yet compact initial representation for characterizing natural textures. The joint location and orientation required to encode the LOIDs is preserved by using moving frames (MFs) texture representations built from locally-steered image gradients that are invariant to rigid motions. In a second step, we use support vector machines to learn a multi-class shaping matrix for the initial CHW representation, yielding data-driven MFs called steerable wavelet machines (SWMs). The SWM forward function is composed of linear operations (i.e., convolution and weighted combinations) interleaved with non-linear steermax operations. We experimentally demonstrate the effectiveness of the proposed operators for classifying natural textures. Our scheme outperforms recent approaches on several test suites of the Outex and the CUReT databases.

Transforms and Operators for Directional Bioimage Analysis: A Survey.

We give a methodology-oriented perspective on directional image analysis and rotation-invariant processing. We review the state of the art in the field and make connections with recent mathematical developments in functional analysis and wavelet theory. We unify our perspective within a common framework using operators. The intent is to provide image-processing methods that can be deployed in algorithms that analyze biomedical images with improved rotation invariance and high directional sensitivity. We start our survey with classical methods such as directional-gradient and the structure tensor. Then, we discuss how these methods can be improved with respect to robustness, invariance to geometric transformations (with a particular interest in scaling), and computation cost. To address robustness against noise, we move forward to higher degrees of directional selectivity and discuss Hessian-based detection schemes. To present multiscale approaches, we explain the differences between Fourier filters, directional wavelets, curvelets, and shearlets. To reduce the computational cost, we address the problem of matching directional patterns by proposing steerable filters, where one might perform arbitrary rotations and optimizations without discretizing the orientation. We define the property of steerability and give an introduction to the design of steerable filters. We cover the spectrum from simple steerable filters through pyramid schemes up to steerable wavelets. We also present illustrations on the design of steerable wavelets and their application to pattern recognition.

SpotCaliper: fast wavelet-based spot detection with accurate size estimation.

MOTIVATION: SpotCaliper is a novel wavelet-based image-analysis software providing a fast automatic detection scheme for circular patterns (spots), combined with the precise estimation of their size. It is implemented as an ImageJ plugin with a friendly user interface. The user is allowed to edit the results by modifying the measurements (in a semi-automated way), extract data for further analysis. The fine tuning of the detections includes the possibility of adjusting or removing the original detections, as well as adding further spots. RESULTS: The main advantage of the software is its ability to capture the size of spots in a fast and accurate way. AVAILABILITY AND IMPLEMENTATION: http://bigwww.epfl.ch/algorithms/spotcaliper/ CONTACT: zsuzsanna.puspoki@epfl.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Design of Steerable Wavelets to Detect Multifold Junctions.

We propose a framework for the detection of junctions in images. Although the detection of edges and key points is a well examined and described area, the multiscale detection of junction centers, especially for odd orders, poses a challenge in pattern analysis. The goal of this paper is to build optimal junction detectors based on 2D steerable wavelets that are polar-separable in the Fourier domain. The approaches we develop are general and can be used for the detection of arbitrary symmetric and asymmetric junctions. The backbone of our construction is a multiscale pyramid with a radial wavelet function where the directional components are represented by circular harmonics and encoded in a shaping matrix. We are able to detect M -fold junctions in different scales and orientations. We provide experimental results on both simulated and real data to demonstrate the effectiveness of the algorithm.

Template-free wavelet-based detection of local symmetries.

Our goal is to detect and group different kinds of local symmetries in images in a scale- and rotation-invariant way. We propose an efficient wavelet-based method to determine the order of local symmetry at each location. Our algorithm relies on circular harmonic wavelets which are used to generate steerable wavelet channels corresponding to different symmetry orders. To give a measure of local symmetry, we use the F-test to examine the distribution of the energy across different channels. We provide experimental results on synthetic images, biological micrographs, and electron-microscopy images to demonstrate the performance of the algorithm.