An Autostereoscopic 3 0 viewing .system that operates on the principles of Integral PhotographyIP provides a imique sen.se of depth. full parallax and multi-view functionality. The inherent redundancy of these images results into great amounts of data that should be efficiently coded for transmission or storage operations. In this communication a method for efficient coding of such images is presented, targeting to 3 0 imaging but video applications. The method is based on common techniques broadly used in image compression and properly adjusted in order to take advantage of the spatial redundancies of IP images. The generalig and flexibility of the proposed approach along with the stability f a r a wide range of bit rates constitutes the basic characteristics of the technique. The proposed technique can be easily realized in sofmare or hardn'are for computer based or standalone applications.

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95. Keywords:Computer-Aided Diagnosis, Ultrasound, Thyroid Nodules, Boundary Features. IntroductionThe results of clinical research demonstrate that the presence of blurred or irregular thyroid nodule boundaries on ultrasound (US) images correlate with malignancy risk [1], [2]. In this light, the quantification of nodule boundary irregularity by boundary-based features could be valuable for malignancy risk assessment, contributing to the objectification of medical decisions. Such boundary-based features could be combined with intensity and textural information within an integrated computer-aided-diagnosis (CAD) tool.Previous attempts on CAD categorization of thyroid nodules on US images include evaluation of parameters from the gray level histogram of thyroid US images [3], [4], intensity features extracted by the utilization of Radon transform [5], textural features extracted from gray level spatial-dependence matrices [6], [7], and the application of discriminant

Integral imaging (InIm) is a highly promising technique for the delivery of three-dimensional (3D) image content. During capturing, different views of an object are recorded as an array of elemental images (EIs), which form the integral image. High-resolution InIm requires sensors with increased resolution and produces huge amounts of highly correlated data. In an efficient encoding scheme for InIm compression both inter-EI and intra-EI correlations have to be properly exploited. We present an EI traversal scheme that maximizes the performance of InIm encoders by properly rearranging EIs to increase the intra-EI correlation of jointly coded EIs. This technique can be used to augment performance of both InIm specific and properly adapted general use encoder setups, used in InIm compression. An objective quality metric is also introduced for evaluating the effects of different traversal schemes on the encoder performance.

In most integral image analysis and processing tasks, accurate knowledge of the internal image structure is required. In this paper we present a robust framework for the accurate rectification of perspectively distorted integral images based on multiple line segment detection. The use of multiple line segments increases the overall fault tolerance of our framework providing strong statistical support for the rectification process. The proposed framework is used for the automatic rectification, metric correction, and rotation of distorted integral images. The performance of our framework is assessed over a number of integral images with varying scene complexity and noise levels.

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