Automated edge-detection technique for measurement of brachial artery reactivity: A comparison of concordance with manual measurements

Concerns have been raised about the reproducibility of brachial artery reactivity (BAR), because subjective decisions regarding the location of interfaces may influence the measurement of very small changes in lumen diameter. We studied 120 consecutive patients with BAR to address if an automated technique could be applied, and if experience influenced reproducibility between two observers, one experienced and one inexperienced. Digital cineloops were measured automatically, using software that measures the leading edge of the endothelium and tracks this in sequential frames and also manually, where a set of three point-to-point measurements were averaged. There was a high correlation between automated and manual techniques for both observers, although less variability was present with expert readers. The limits of agreement overall for interobserver concordance were 0.13 +/-0.65 mm for the manual and 0.03 +/-0.74 mm for the automated measurement. For intraobserver concordance, the limits of agreement were -0.07 +/-0.38 mm for observer 1 and -0.16 +/-0.55 mm for observer 2. We concluded that BAR measurements were highly concordant between observers, although more concordant using the automated method, and that experience does affect concordance. Care must be taken to ensure that the same segments are measured between observers and serially.

Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Edge detection by selection of pieces of level lines

We propose an edge detector based on the selection of wellcontrasted pieces of level lines, following the proposal ofDesolneux-Moisan-Morel (DMM) [1]. The DMM edge detectorhas the problem of over-representation, that is, everyedge is detected several times in slightly different positions.In this paper we propose two modifications of the originalDMM edge detector in order to solve this problem. The firstmodification is a post-processing of the output using a generalmethod to select the best representative of a bundle of curves.The second modification is the use of Canny’s edge detectorinstead of the norm of the gradient to build the statistics. Thetwo modifications are independent and can be applied separately.Elementary reasoning and some experiments showthat the best results are obtained when both modifications areapplied together.

Detection and thinning of edges in previously known directions

This work presents a study on the generation of digital masks aiming at edge detection with previously known directions. This solution is important when edge direction is available either from a direction histogram or from a prediction based on camera and object models. A modification in the non-maximum suppression method of thinning is also presented enabling the comparison of local maxima for any edge directions. Results with a synthetic image and with crops of a CBERS satellite images are presented showing an example with its application in road detection, provided that directions are previously known.

Edge detection and noise removal by use of a partial differential equation with automatic selection of parameters

This work deals with noise removal by the use of an edge preserving method whose parameters are automatically estimated, for any application, by simply providing information about the standard deviation noise level we wish to eliminate. The desired noiseless image u(x), in a Partial Differential Equation based model, can be viewed as the solution of an evolutionary differential equation u t(x) = F(u xx, u x, u, x, t) which means that the true solution will be reached when t ® ¥. In practical applications we should stop the time ''t'' at some moment during this evolutionary process. This work presents a sufficient condition, related to time t and to the standard deviation s of the noise we desire to remove, which gives a constant T such that u(x, T) is a good approximation of u(x). The approach here focused on edge preservation during the noise elimination process as its main characteristic. The balance between edge points and interior points is carried out by a function g which depends on the initial noisy image u(x, t0), the standard deviation of the noise we want to eliminate and a constant k. The k parameter estimation is also presented in this work therefore making, the proposed model automatic. The model's feasibility and the choice of the optimal time scale is evident through out the various experimental results.

A well-balanced flow equation for noise removal and edge detection

In this paper, an anisotropic nonlinear diffusion equation for image restoration is presented. The model has two terms: the diffusion and the forcing term. The balance between these terms is made in a selective way, in which boundary points and interior points of the objects that make up the image are treated differently. The optimal smoothing time concept, which allows for finding the ideal stop time for the evolution of the partial differential equation is also proposed. Numerical results show the proposed model's high performance.

Edge detection in digital images using fuzzy numbers

The purpose of this paper is to introduce a new approach for edge detection in grey shaded images. The proposed approach is based on the fuzzy number theory. The idea is to deal with the uncertainties concerning the grey shades making up the image and, thus, calculate the appropriateness of the pixels in relation to a homogeneous region around them. The pixels not belonging to the region are then classified as border pixels. The results have shown that the technique is simple, computationally efficient and with good results when compared with both the traditional border detectors and the fuzzy edge detectors. Copyright © 2009, Inderscience Publishers.

BOLD correlates of edge detection in human auditory cortex

Edges are important cues defining coherent auditory objects. As a model of auditory edges, sound on- and offset are particularly suitable to study their neural underpinnings because they contrast a specific physical input against no physical input. Change from silence to sound, that is onset, has extensively been studied and elicits transient neural responses bilaterally in auditory cortex. However, neural activity associated with sound onset is not only related to edge detection but also to novel afferent inputs. Edges at the change from sound to silence, that is offset, are not confounded by novel physical input and thus allow to examine neural activity associated with sound edges per se. In the first experiment, we used silent acquisition functional magnetic resonance imaging and found that the offset of pulsed sound activates planum temporale, superior temporal sulcus and planum polare of the right hemisphere. In the planum temporale and the superior temporal sulcus, offset response amplitudes were related to the pulse repetition rate of the preceding stimulation. In the second experiment, we found that these offset-responsive regions were also activated by single sound pulses, onset of sound pulse sequences and single sound pulse omissions within sound pulse sequences. However, they were not active during sustained sound presentation. Thus, our data show that circumscribed areas in right temporal cortex are specifically involved in identifying auditory edges. This operation is crucial for translating acoustic signal time series into coherent auditory objects.

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

In Llanas and Lantarón, J. Sci. Comput. 46, 485–518 (2011) we proposed an algorithm (EDAS-d) to approximate the jump discontinuity set of functions defined on subsets of ℝ d . This procedure is based on adaptive splitting of the domain of the function guided by the value of an average integral. The above study was limited to the 1D and 2D versions of the algorithm. In this paper we address the three-dimensional problem. We prove an integral inequality (in the case d=3) which constitutes the basis of EDAS-3. We have performed detailed computational experiments demonstrating effective edge detection in 3D function models with different interface topologies. EDAS-1 and EDAS-2 appealing properties are extensible to the 3D case

Analysis of edge detection algorithms on DIAL /

Mode of access: Internet.

Mach edges: a critical test of the nonlinear 3rd derivative model for edge-detection

Feature detection is a crucial stage of visual processing. In previous feature-marking experiments we found that peaks in the 3rd derivative of the luminance profile can signify edges where there are no 1st derivative peaks nor 2nd derivative zero-crossings (Wallis and George 'Mach edges' (the edges of Mach bands) were nicely predicted by a new nonlinear model based on 3rd derivative filtering. As a critical test of the model, we now use a new class of stimuli, formed by adding a linear luminance ramp to the blurred triangle waves used previously. The ramp has no effect on the second or higher derivatives, but the nonlinear model predicts a shift from seeing two edges to seeing only one edge as the added ramp gradient increases. In experiment 1, subjects judged whether one or two edges were visible on each trial. In experiment 2, subjects used a cursor to mark perceived edges and bars. The position and polarity of the marked edges were close to model predictions. Both experiments produced the predicted shift from two to one Mach edge, but the shift was less complete than predicted. We conclude that the model is a useful predictor of edge perception, but needs some modification.

Application of Fuzzy Logic on Image Edge Detection

In this paper a novel method for an application of digital image processing, Edge Detection is developed. The contemporary Fuzzy logic, a key concept of artificial intelligence helps to implement the fuzzy relative pixel value algorithms and helps to find and highlight all the edges associated with an image by checking the relative pixel values and thus provides an algorithm to abridge the concepts of digital image processing and artificial intelligence. Exhaustive scanning of an image using the windowing technique takes place which is subjected to a set of fuzzy conditions for the comparison of pixel values with adjacent pixels to check the pixel magnitude gradient in the window. After the testing of fuzzy conditions the appropriate values are allocated to the pixels in the window under testing to provide an image highlighted with all the associated edges.

A subpixel edge detector applied to aortic dissection detection

[EN] The aortic dissection is a disease that can cause a deadly situation, even with a correct treatment. It consists in a rupture of a layer of the aortic artery wall, causing a blood flow inside this rupture, called dissection. The aim of this paper is to contribute to its diagnosis, detecting the dissection edges inside the aorta. A subpixel accuracy edge detector based on the hypothesis of partial volume effect is used, where the intensity of an edge pixel is the sum of the contribution of each color weighted by its relative area inside the pixel. The method uses a floating window centred on the edge pixel and computes the edge features. The accuracy of our method is evaluated on synthetic images of different hickness and noise levels, obtaining an edge detection with a maximal mean error lower than 16 percent of a pixel.

Radon-domain detection of the nipple and the pectoral muscle in mammograms

In this paper, methods are presented for automatic detection of the nipple and the pectoral muscle edge in mammograms via image processing in the Radon domain. Radon-domain information was used for the detection of straight-line candidates with high gradient. The longest straight-line candidate was used to identify the pectoral muscle edge. The nipple was detected as the convergence point of breast tissue components, indicated by the largest response in the Radon domain. Percentages of false-positive (FP) and false-negative (FN) areas were determined by comparing the areas of the pectoral muscle regions delimited manually by a radiologist and by the proposed method applied to 540 mediolateral-oblique (MLO) mammographic images. The average FP and FN were 8.99% and 9.13%, respectively. In the detection of the nipple, an average error of 7.4 mm was obtained with reference to the nipple as identified by a radiologist on 1,080 mammographic images (540 MLO and 540 craniocaudal views).