3,690 research outputs found

    Bilateral filter in image processing

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    The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges. It has shown to be an effective image denoising technique. It also can be applied to the blocking artifacts reduction. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. Another research interest of bilateral filter is acceleration of the computation speed. There are three main contributions of this thesis. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising. I propose an extension of the bilateral filter: multi resolution bilateral filter, where bilateral filtering is applied to the low-frequency sub-bands of a signal decomposed using a wavelet filter bank. The multi resolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. The second contribution is that I present a spatially adaptive method to reduce compression artifacts. To avoid over-smoothing texture regions and to effectively eliminate blocking and ringing artifacts, in this paper, texture regions and block boundary discontinuities are first detected; these are then used to control/adapt the spatial and intensity parameters of the bilateral filter. The test results prove that the adaptive method can improve the quality of restored images significantly better than the standard bilateral filter. The third contribution is the improvement of the fast bilateral filter, in which I use a combination of multi windows to approximate the Gaussian filter more precisely

    Automated detection of extended sources in radio maps: progress from the SCORPIO survey

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    Automated source extraction and parameterization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parameterization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, including also different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU survey. The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure

    Analysis of Amoeba Active Contours

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    Subject of this paper is the theoretical analysis of structure-adaptive median filter algorithms that approximate curvature-based PDEs for image filtering and segmentation. These so-called morphological amoeba filters are based on a concept introduced by Lerallut et al. They achieve similar results as the well-known geodesic active contour and self-snakes PDEs. In the present work, the PDE approximated by amoeba active contours is derived for a general geometric situation and general amoeba metric. This PDE is structurally similar but not identical to the geodesic active contour equation. It reproduces the previous PDE approximation results for amoeba median filters as special cases. Furthermore, modifications of the basic amoeba active contour algorithm are analysed that are related to the morphological force terms frequently used with geodesic active contours. Experiments demonstrate the basic behaviour of amoeba active contours and its similarity to geodesic active contours.Comment: Revised version with several improvements for clarity, slightly extended experiments and discussion. Accepted for publication in Journal of Mathematical Imaging and Visio

    Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery

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    Abstract—The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMTmean ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques

    Probing for Exoplanets Hiding in Dusty Debris Disks: Disk Imaging, Characterization, and Exploration with HST/STIS Multi-Roll Coronagraphy

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    Spatially resolved scattered-light images of circumstellar (CS) debris in exoplanetary systems constrain the physical properties and orbits of the dust particles in these systems. They also inform on co-orbiting (but unseen) planets, systemic architectures, and forces perturbing starlight-scattering CS material. Using HST/STIS optical coronagraphy, we have completed the observational phase of a program to study the spatial distribution of dust in ten CS debris systems, and one "mature" protoplanetrary disk all with HST pedigree, using PSF-subtracted multi-roll coronagraphy. These observations probe stellocentric distances > 5 AU for the nearest stars, and simultaneously resolve disk substructures well beyond, corresponding to the giant planet and Kuiper belt regions in our Solar System. They also disclose diffuse very low-surface brightness dust at larger stellocentric distances. We present new results inclusive of fainter disks such as HD92945 confirming, and better revealing, the existence of a narrow inner debris ring within a larger diffuse dust disk. Other disks with ring-like sub-structures, significant asymmetries and complex morphologies include: HD181327 with a posited spray of ejecta from a recent massive collision in an exo-Kuiper belt; HD61005 suggested interacting with the local ISM; HD15115 & HD32297, discussed also in the context of environmental interactions. These disks, and HD15745, suggest debris system evolution cannot be treated in isolation. For AU Mic's edge-on disk, out-of-plane surface brightness asymmetries at > 5 AU may implicate one or more planetary perturbers. Time resolved images of the MP Mus proto-planetary disk provide spatially resolved temporal variability in the disk illumination. These and other new images from our program enable direct inter-comparison of the architectures of these exoplanetary debris systems in the context of our own Solar System.Comment: 109 pages, 43 figures, accepted for publication in the Astronomical Journa

    General Adaptive Neighborhood Image Processing. Part II: Practical Applications Issues

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    23 pagesInternational audienceThe so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively with its theoretical and practical aspects. The General Adaptive Neighborhood (GAN) paradigm, theoretically introduced in Part I [20], allows the building of new image processing transformations using context-dependent analysis. With the help of a specified analyzing criterion, such transformations perform a more significant spatial analysis, taking intrinsically into account the local radiometric, morphological or geometrical characteristics of the image. Moreover they are consistent with the physical and/or physiological settings of the image to be processed, using general linear image processing frameworks. In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting morphological operators perform a really spatiallyadaptive image processing and notably, in several important and practical cases, are connected, which is a great advantage compared to the usual ones that fail to this property. Several GANIP-based results are here exposed and discussed in image filtering, image segmentation, and image enhancement. In order to evaluate the proposed approach, a comparative study is as far as possible proposed between the adaptive and usual morphological operators. Moreover, the interests to work with the Logarithmic Image Processing framework and with the 'contrast' criterion are shown through practical application examples

    Live and Dead Cells Counting from Microscopic Trypan Blue Staining Images using Thresholding and Morphological Operation Techniques

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    Cell counting is a required procedure in biomedical experiments and drug testing. Manual cell counting performed with a hemocytometer is time consuming and individual dependence. This study reportedthe development of a computer-assisted program for trypan blue stained-cell counting using digital image analysis. Images of trypan blue-stained breast cancer cells line were obtained by a microscope with a digital camera. Undesired noise and debris were removed by applying a guided image filter. Color space HSV (Hue, Saturation and Value)conversion and grayscale conversion were performed for distinguishing between live and dead cells.  Image thresholding and morphological operators were applied for image segmentation. Live and dead cells were counted after image segmentation and the results were compared with manual counting by three well-experienced counters. The computer-assisted cell counting from thirty-six trypan blue-stained microscopic images had a high correlation coefficient with the live cell results of the experts (r=0.99). The correlation coefficient of the number of dead cells comparing the computer-assisted count and the experts’ count was 0.74. Our approach offers high accuracy (>85%)on counting live cells compared with the experts’ counting. This automated cell counting approach can assist biomedical researchers for both live and dead cells counting

    Amoeba Techniques for Shape and Texture Analysis

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    Morphological amoebas are image-adaptive structuring elements for morphological and other local image filters introduced by Lerallut et al. Their construction is based on combining spatial distance with contrast information into an image-dependent metric. Amoeba filters show interesting parallels to image filtering methods based on partial differential equations (PDEs), which can be confirmed by asymptotic equivalence results. In computing amoebas, graph structures are generated that hold information about local image texture. This paper reviews and summarises the work of the author and his coauthors on morphological amoebas, particularly their relations to PDE filters and texture analysis. It presents some extensions and points out directions for future investigation on the subject.Comment: 38 pages, 19 figures v2: minor corrections and rephrasing, Section 5 (pre-smoothing) extende
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