segmentation of the spine directly from three-dimensional (3-D) image data is desirable to accurately capture its morphological properties. We describe a method that allows true 3-D spinal imagesegmentation using a d...
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segmentation of the spine directly from three-dimensional (3-D) image data is desirable to accurately capture its morphological properties. We describe a method that allows true 3-D spinal imagesegmentation using a deformable integral spine model. The method learns the appearance of vertebrae from multiple continuous features recorded along vertebra boundaries in a given training set of images. Important summarizing statistics are encoded into a necklace model on which landmarks are differentiated on their free dimensions. The landmarks are used within a priority segmentation scheme to reduce the complexity of the segmentation problem. Necklace models are coupled by string models. The string models describe in detail the biological variability in the appearance of spinal curvatures from multiple continuous features recorded in the training set. In the segmentation phase, the necklace and string models are used to interactively detect vertebral structures in new image data via elastic deformation reminiscent of a marionette with strings allowing for movement between interrelated structures. Strings constrain the deformation of the spine model within feasible solutions. The driving application in this work is analysis of computed tomography scans of the human lumbar spine. An illustration of the segmentation process shows that the method is promising for segmentation of the spine and for assessment of its morphological properties.
In this paper, we present Lazy Snapping, an interactiveimage cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy. Moreover, Lazy Snapping pr...
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In this paper, we present Lazy Snapping, an interactiveimage cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy. Moreover, Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges. Instant feedback is made possible by a novel imagesegmentation algorithm which combines graph cut with pre-computed over-segmentation. A set of intuitive user interface (UI) tools is designed and implemented to provide flexible control and editing for the users. Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactiveimage cutout tool, Magnetic Lasso in Adobe Photoshop.
The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical imagesegmentation tools use either texture (colour) information, e...
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The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical imagesegmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.
In this paper, we present Lazy Snapping, an interactiveimage cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy. Moreover, Lazy Snapping pr...
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In this paper, we present Lazy Snapping, an interactiveimage cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy. Moreover, Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges. Instant feedback is made possible by a novel imagesegmentation algorithm which combines graph cut with pre-computed over-segmentation. A set of intuitive user interface (UI) tools is designed and implemented to provide flexible control and editing for the users. Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactiveimage cutout tool, Magnetic Lasso in Adobe Photoshop.
The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical imagesegmentation tools use either texture (colour) information, e...
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The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical imagesegmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.
Medical image acquisition devices are becoming increasingly multidimensional, and predictions assume 5600 images per patient study within the next decade. Therefore, new paradigms for computerized medical image analys...
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Medical image acquisition devices are becoming increasingly multidimensional, and predictions assume 5600 images per patient study within the next decade. Therefore, new paradigms for computerized medical image analysis and visualization are of fundamental importance to make possibly full use of the information buried in the enormous flood of image data. The aim of the presented project is the optimal cooperation between computer-based image,analysis algorithms and human operators using new closed-loop segmentation systems for improved information flow. This paper describes an enhanced, visuo-haptic interaction tool we have developed for medical segmentation. Evaluation studies with the system, which confirm the value of adding haptic feedback, are also presented.
For an interactive, computer-aided imagesegmentation tool to work convincingly, the edges marked by it should, where possible, match those observed by the user on the displayed image (illusory edges excluded). An imp...
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For an interactive, computer-aided imagesegmentation tool to work convincingly, the edges marked by it should, where possible, match those observed by the user on the displayed image (illusory edges excluded). An improvement in edge correspondence can be obtained by calculating observed luminance contrasts. These account for both the non-linearity of the screen and for the veiling effect of room illumination on the displayed image. It is shown how a logarithmic transformation of the photopic luminance data can be used in conjunction with the Canny operator to produce a visually convincing contrast edge map.
The aim of this work is to bring the computer marking of edges (on grey images) into closer correspondence with the perception of edges when an image is viewed on a typical computer screen under normal lighting condit...
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The aim of this work is to bring the computer marking of edges (on grey images) into closer correspondence with the perception of edges when an image is viewed on a typical computer screen under normal lighting conditions. To obtain the required correspondence, the computer should start off with comparable source. data to the human, and perform similar edge-detecting operations on that data. Calculation of the amount of stimulation received by the eye requires knowledge of the screen's photopic emission, the amount of ambient light reflected from the screen, and the eye's sensitivity to luminance contrast. The contrast calculation used was derived from Michelson contrast by separating out a term for the veiling light, L(veil). The contrast, here called veiled-Michelson contrast C(vM), is defined as C(vM) = (L(dmax) - L(dmin))/L(dmax) + L(dmin) + 2L(veil)), where L(dmax) is the greater of the two photopic luminances emitted by the screen in darkness. The results of this calculation are compared with those of a step-difference operator, and show a qualitively improved correspondence with the visual perception.
Práce se zabývá technikami pro interaktivní segmentaci popředí a pozadí na mobilních zařízeních. Cílem je nalézt nebo vytvořit vhodné metody pro oddělen...
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Práce se zabývá technikami pro interaktivní segmentaci popředí a pozadí na mobilních zařízeních. Cílem je nalézt nebo vytvořit vhodné metody pro oddělení uživatelem označeného objektu od pozadí. V úvahu je brána zejména rychlost segmentace a kvalita výstupu. Jsou popsány principy již existujících metod pro interaktivní segmentaci, je diskutováno jejich využití na mobilních zařízeních. Je navržena a implementována vlastní metoda velmi rychlé segmentace použitá pro náhled z kamery mobilního zařízení v reálném čase.
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