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Number of results: 9
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Abstract

Analysis of the shape and location of abrasive grain tips as well as their changes during the grinding process, is the basis for forecasting the machining process results. This paper presents a methodology of using the watershed segmentation in identifying abrasive grains on the abrasive tool active surface. Some abrasive grain tips were selected to minimize the errors of detecting many tips on a single abrasive grain. The abrasive grains, singled out as a result of the watershed segmentation, were then analyzed to determine their geometric parameters. Moreover, the statistical parameters describing their locations on the abrasive tool active surface and the parameters characterizing intergranular spaces were determined.
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Abstract

This paper presents the improved version of the classification system for supporting glaucoma diagnosis in ophthalmology. In this paper we propose the new segmentation step based on the support vector clustering algorithm which enables better classification performance.
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Abstract

With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the Level Set (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.
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Abstract

Cardiovascular system diseases are the major causes of mortality in the world. The most important and widely used tool for assessing the heart state is echocardiography (also abbreviated as ECHO). ECHO images are used e.g. for location of any damage of heart tissues, in calculation of cardiac tissue displacement at any arbitrary point and to derive useful heart parameters like size and shape, cardiac output, ejection fraction, pumping capacity. In this paper, a robust algorithm for heart shape estimation (segmentation) in ECHO images is proposed. It is based on the recently introduced variant of the level set method called level set without edges. This variant takes advantage of the intensity value of area information instead of module of gradient which is typically used. Such approach guarantees stability and correctness of algorithm working on the border between object and background with small absolute value of image gradient. To reassure meaningful results, the image segmentation is proceeded with automatic Region of Interest (ROI) calculation. The main idea of ROI calculations is to receive a triangle-like part of the acquired ECHO image, using linear Hough transform, thresholding and simple mathematics. Additionally, in order to improve the images quality, an anisotropic diffusion filter, before ROI calculation, was used. The proposed method has been tested on real echocardiographic image sequences. Derived results confirm the effectiveness of the presented method.
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Abstract

The study of the different engineering materials according to their mechanical and dynamic characteristics has become an area of research interest in recent years. Several studies have verified that the mechanical properties of the material are directly affected by the distribution and size of the particles that compose it. Such is the case of asphalt mixtures. For this reason, different digital tools have been developed in order to be able to detect the structural components of the elements in a precise, clear and efficient manner. In this work, a segmentation model is developed for different types of dense-graded asphalt mixtures with grain sizes from 9.5 mm to 0.0075 mm, using sieve size reconstruction of the laboratory production curve. The laboratory curve is used to validate the particles detection model that uses morphological operations for elements separation. All this with the objective of developing a versatile tool for the analysis and study of pavement structures in a non-destructive test. The results show that the model presented in this work is able to segment elements with an area greater than 0.0324 mm2 and reproduce the sieve size curves of the mixtures with a high percentage of precision.
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Abstract

This paper presents signal processing aspects for automatic segmentation of retinal layers of the human eye. The paper draws attention to the problems that occur during the computer image processing of images obtained with the use of the Spectral Domain Optical Coherence Tomography (SD OCT). Accuracy of the retinal layer segmentation for a set of typical 3D scans with a rather low quality was shown. Some possible ways to improve quality of the final results are pointed out. The experimental studies were performed using the so-called B-scans obtained with the OCT Copernicus HR device.
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Abstract

In this paper, a modification of the graph-based depth estimation is presented. The purpose of proposed modification is to increase the quality of estimated depth maps, reduce the time of the estimation, and increase the temporal consistency of depth maps. The modification is based on the image segmentation using superpixels, therefore in the first step of the proposed modification a segmentation of previous frames is used in the currently processed frame in order to reduce the overall time of the depth estimation. In the next step, a depth map from the previous frame is used in the depth map optimization as the initial values of a depth map estimated for the current frame. It results in the better representation of silhouettes of objects in depth maps and in the reduced computational complexity of the depth estimation process. In order to evaluate the performance of the proposed modification the authors performed the experiment for a set of multiview test sequences that varied in their content and an arrangement of cameras. The results of the experiments confirmed the increase of the depth maps quality — the quality of depth maps calculated with the proposed modification is higher than for the unmodified depth estimation method, apart from the number of the performed optimization cycles. Therefore, use of the proposed modification allows to estimate a depth of the better quality with almost 40% reduction of the estimation time. Moreover, the temporal consistency, measured through the reduction of the bitrate of encoded virtual views, was also considerably increased.
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