Catálogo de publicaciones - libros

This paper presents a novel adaptive vision system for accurate segmentation of tissue structures in echographic medical images. The proposed vision system incorporates a level-set deformable model based on a modified Mumford-Shah functional, which is estimated over sparse foreground and background regions in the image. This functional is designed so that it copes with the intensity inhomogeneity that characterizes echographic medical images. Moreover, a parameter tuning mechanism has been considered for the adaptation of the deformable model parameters. Experiments were conducted over a range of echographic images displaying abnormal structures of the breast and of the thyroid gland. The results show that the proposed adaptive vision system stands as an efficient, effective and nearly objective tool for segmentation of echographic images.

In this paper we study how shape information encoded in contour energy components values can be used for detection of microscopic organisms in population images. We proposed features based on shape and geometrical statistical data obtained from samples of optimized contour lines integrated in the framework of Bayesian inference for recognition of individual specimens. Compared with common geometric features the results show that patterns present in the image allow better detection of a considerable amount of individuals even in cluttered regions when sufficient shape information is retained. Therefore providing an alternative to building a specific shape model or imposing specific constrains on the interaction of overlapping objects.

Digital capture with consumer digital still camera of the radiographic film significantly decreases the dynamic range and, hence, the details visibility. We propose a method that boosts the dynamic range of the processed X-ray image based on the fusion of a set of digital images acquired under different exposure values. The fusion is controlled by a fuzzy-like confidence information and the luminance range is over-sampled by using logarithmic image processing operators.

We address the problem of pixel classification in fluorescence microscopy images by only using wavelength information. To achieve this, we use Support Vector Machines as supervised classifiers and pixels components as feature vectors. We propose a representation derived from the HSV color space that allows separation between color and intensity information. An extension of this transformation is also presented that allows to performs an a priori object/background segmentation. We show that these transformations not only allows intensity independent classification but also makes the classification problem more simple. As an illustration, we perform intensity independent pixel classification first on a synthetic then on real biological images.

Using cylindrical parameterization, the 3D mesh surface extracted from colon CT scan images is parameterized onto a cylinder, and afterwards visualized with a modified Chamfer distance transformation of the original CT images with regards to the colon centerline/boundary distance. The cylinder with information from distance transformation is then unfolded with numerical integration along its circumferential direction and mapped to a plane, which approximates the view of a colon cut open along its length.

In this paper, we present a particle filter based 2D/3D reconstruction scheme combining a parameterized multiple-component geometrical model and a point distribution model, and show its application to automatically reconstruct a surface model of a proximal femur from a limited number of calibrated X-ray images with no user intervention at all. The parameterized multiple-component geometrical model is regarded as a simplified description capturing the geometrical features of a proximal femur. Its parameters are optimally and automatically estimated from the input images using a particle filter based algorithm. The estimated geometrical parameters are then used to initialize a point distribution model based 2D/3D reconstruction scheme for an accurate reconstruction of a surface model of the proximal femur. We designed and conducted and to compare the present automatic reconstruction scheme to a manually initialized one. An average mean reconstruction error of 1.2 mm was found when the manually initialized reconstruction scheme was used. It increased to 1.3 mm when the automatic one was used. However, the automatic reconstruction scheme has the advantage of elimination of user intervention, which holds the potential to facilitate the application of the 2D/3D reconstruction in surgical navigation.

This paper presents a new method to both track and segment objects in videos. It includes predictions and observations inside an energy function that is minimized with graph cuts. The min-cut/max-flow algorithm provides a segmentation as the global minimum of the energy function, at a modest computational cost. Simultaneously, our algorithm associates the tracked objects to the observations during the tracking. It thus combines “detect-before-track” tracking algorithms and segmentation methods based on color/motion distributions and/or temporal consistency. Results on real sequences are presented in which the robustness to partial occlusions and to missing observations is shown.

In this paper a new low-complexity algorithm for the denoising of video sequences is presented. The proposed fuzzy-rule based algorithm is first explained in the pixel domain and later extended to the wavelet domain. The method can be seen as a fuzzy variant of a recent multiple class video denoising method that automatically adapts to detail and motion. Experimental results show that the proposed algorithm efficiently removes Gaussian noise from digital greyscale image sequences. These results also show that our method outperforms other state-of-the-art filters of comparable complexity for different video sequences.

Video scalability plays an increasingly important role in the disclosure of digital video content. Currently, the scalable extension of the H.264/AVC video coding standard (SVC) is being finalized, which provides scalability layers for state-of-the-art H.264/AVC video streams. Existing video content that is coded using single-layer H.264/AVC, however, cannot benefit from the newly developed scalability features. Here, we discuss our architecture for H.264/AVC-to-SVC transcoding, which is able to derive SNR scalability layers from existing H.264/AVC bitstreams. Results show that the rate-distortion performance of our architecture approaches the optimal decoder-encoder cascade within 1 to 2 dB. Timing results indicate that intelligent conversion techniques are required, and that transcoding can significantly reduce the required computation time.

In some video coding applications, it is desirable to reduce the complexity of the video encoder at the expense of a more complex decoder. Distributed Video (DV) Coding is a new paradigm that aims at achieving this. To allocate a proper number of bits to each frame, most DV coding algorithms use a feedback channel (FBC). However, in some cases, a FBC does not exist. In this paper, we therefore propose a rate allocation (RA) algorithm for pixel-domain distributed video (PDDV) coders without FBC. Our algorithm estimates at the encoder the number of bits for every frame without significantly increasing the encoder complexity. For this calculation we consider each pixel of the frame individually, in contrast to our earlier work where the whole frame is treated jointly. Experimental results show that this pixel-based approach delivers better estimates of the adequate encoding rate than the frame-based approach. Compared to the PDDV coder with FBC, the PDDV coder without FBC has only a small loss in RD performance, especially at low rates.