Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding.

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.

Hierarchical representation and coding of surfaces using 3-D polygon meshes.

This paper presents a novel procedure for the representation and coding of three-dimensional (3-D) surfaces using hierarchical adaptive triangulation. The proposed procedure is based on pyramidal analysis using the quincunx sampling minimum variance interpolation (QMVINT) filters. These are reduced pyramids with quincunx sampling applied to the parametric representation of the surface, chosen so as to minimize the variance of the interpolation error, and thus, when combined with the appropriate encoding of the coefficients, optimize the compression of the mesh information transmitted. At the same time, it produces a hierarchy of meshes based on quincunx sampling where coarse meshes are as similar to their finer versions as possible. This is very much desirable in progressive transmission. Depending on its interpolation error and the available bitrate, each filtered sample is a candidate for becoming a vertex of the mesh. The result is a progressive sequence of meshes consisting of more triangles wherever large variations exist and fewer in uniform regions. Complete correspondence between triangles at each level is identified, resulting in an efficient hierarchical representation of the mesh. The algorithm can be also used for the triangulation of a specific region of interest. Experimental results demonstrate that the proposed scheme provides an improvement in quality (MSE) by a factor of two when compared with other well known adaptive triangulation schemes.

Deformable boundary detection of stents in angiographic images.

In this paper, a procedure is described for deformable boundary detection of medical tools, called stents, in angiographic images. A stent is a surgical stainless steel coil that is placed in the artery in order to improve blood circulation in regions where a stenosis has appeared. Assuming initially a set of three-dimensional (3-D) models of stents and using perspective projection of various deformations of the 3-D model of the stent, a large set of synthetic two-dimensional (2-D) images of stents is constructed. These synthetic images are then used as a training set for deriving a multivariate Gaussian density estimate based on eigenspace decomposition and formulating a maximum-likelihood estimation framework in order to reach an initial rough estimate for automatic object recognition. The silhouette of the detected stent is then refined by using a 2-D active contour (snake) algorithm integrated with a novel iterative initialization technique, which takes into consideration the geometry of the stent. The algorithm is experimentally evaluated using real angiographic images containing stents.

'LipTelephone' a videophone for the deaf.

This paper describes the 'LipTelephone' system, which is being developed under a Greek AMEA research project, with the purpose to serve as a videophone that can also be used by lip readers. This system aims to combine model-based with traditional coding techniques in order to exploit the information redundancy in a scene of known content, while achieving high fidelity representation in the specific area of interest, which is the speaker's mouth. The recent MPEG-4 coding standard provides the framework, which is employed, for these purposes.

Deformable boundary detection of stents in angiographic images.

In this paper a procedure is described for deformable boundary detection of medical tools, called stents, in angiographic images. A stent is a surgical stainless steel coil which is placed in the artery in order to improve blood circulation, in regions where a stenosis has appeared. Assuming initially a set of 3-D models of stents and using perspective projection of various deformations of the 3-D model of the stent, a large set of synthetic 2D images of stents is constructed. These synthetic images are then used as a training set for deriving a multivariate Gaussian density estimate based on eigenspace decomposition and formulating a Maximum-Likelihood estimation framework in order to reach an initial rough estimate for automatic object recognition. The silhouette of the detected stent is then refined by using a 2D active contour (snake) algorithm integrated with a novel iterative initialization technique which takes into consideration the geometry of the stent. The algorithm is experimentally evaluated using real angiographic images containing stents.

Orientation-sensitive interpolative pyramids for lossless and progressive image coding.

This paper presents a modified JPEG coder that is applied to the compression of mixed documents (containing text, natural images, and graphics) for printing purposes. The modified JPEG coder proposed in this paper takes advantage of the distinct perceptually significant regions in these documents to achieve higher perceptual quality than the standard JPEG coder. The region-adaptivity is performed via classified thresholding being totally compliant with the baseline standard. A computationally efficient classification algorithm is presented, and the improved performance of the classified JPEG coder is verified.

Lossless coding of multichannel signals using optimal vector hierarchical decomposition.

A methodology is presented for the optimal construction of multichannel reduced pyramids by selecting the interpolation synthesis postfilters so as to minimize the error variance at each level of the pyramid. The general optimization methodology is applied for the optimization of pyramids for the compression of electrocardiographic signals and RGB colored images.

Practical evaluation of standard-based low-cost video conferencing in telemedicine and epidemiological applications.

The results of the evaluation of use of low-cost video conferencing systems (VCSs) in telemedicine is presented. Applications sharing, a new feature of these systems, recently has allowed high-quality computer-supported collaborative work (CSCW). The video conferencing (VCing) equipment used was Intel ProShare 200 v2.0a. It is representative of other low-cost VCSs. The areas of application are epidemiology and telemedicine (orthopaedics and radiology). Potential end users filled out 58 evaluation questionnaires concerning user profiles, contents and benefits of the sessions, organizational aspects, user friendliness, user acceptance, cost effectiveness, technical and multipoint related aspects. Although the end users had a lot of computer experience, their knowledge in VCSs was rather limited. The users assessed the system capable of being integrated into routine work, despite a high organizational impact. The VCS is user friendly, application sharing being used in almost every session. Audio quality was not always sufficient. The remote video was sufficient, as was the quality of medical images such as CT, MRI or X-ray. The user acceptance of the system was high. Multipoint sessions require a structured protocol to be effective. Some technical problems with MCUs (Multipoint Control Units) occurred. The use of low-cost standard VCSs in telemedicine is advisable and is a good substitute for real meetings.

Tracking the left ventricle in echocardiographic images by learning heart dynamics.

In this paper a temporal learning-filtering procedure is applied to refine the left ventricle (LV) boundary detected by an active-contour model. Instead of making prior assumptions about the LV shape or its motion, this information is incrementally gathered directly from the images and is exploited to achieve more coherent segmentation. A Hough transform technique is used to find an initial approximation of the object boundary at the first frame of the sequence. Then, an active-contour model is used in a coarse-to-fine framework, for the estimation of a noisy LV boundary. The PCA transform is applied to form a reduced ordered orthonormal basis of the LV deformations based on a sequence of noisy boundary observations. Then this basis is used to constrain the motion of the active contour in subsequent frames, and thus provide more coherent identification. Results of epicardial boundary identification in B-mode images are presented.

Optimal linear compression under unreliable representation and robust PCA neural models.

In a typical linear data compression system the representation variables resulting from the coding operation are assumed totally reliable and therefore the solution in the mean-squared-error sense is an orthogonal projector to the so-called principal component subspace. When the representation variables are contaminated by additive noise which is uncorrelated with the signal, the problem is called noisy principal component analysis (NPCA) and the optimal MSE solution is not a trivial extension of PCA. We first show that the problem is not well defined unless we impose explicit or implicit constraints on either the coding or the decoding operator. Second, orthogonality is not a property of the optimal solution under most constraints. Third, the signal components may or may not be reconstructed depending on the noise level. As the noise power increases, we observe rank reduction in the optimal solution under most reasonable constraints. In these cases it appears that it is preferable to omit the smaller signal components rather than attempting to reconstruct them. This phenomenon has similarities with classical information theoretical results, notably the water-filling analogy, found in parallel additive Gaussian noise channels. Finally, we show that standard Hebbian-type PCA learning algorithms are not optimally robust to noise, and propose a new Hebbian-type learning algorithm which is optimally robust in the NPCA sense.

Optimal biorthogonal wavelet decomposition of wire-frame meshes using box splines, and its application to the hierarchical coding of 3-D surfaces.

Optimal mechanisms are determined for the hierarchical decomposition of wire-frame surfaces generated by box splines. A family of box splines with compact support, suitable for the approximation of wire-frames is first defined, generated by arbitrary sampling matrices with integer eigenvalues. For each such box spline, the optimal positioning of the wire-frame nodes is determined for each level of the hierarchical wire-frame decomposition. Criterion of optimality is the minimization of the variance of the error difference between the original surface and its representation at each resolution level. This is needed so as to ensure that the wire mesh produces at each resolution as close a replica of the original surface as possible. Several such combinations of box spline generated meshes and the corresponding optimal node lattice sequences are examined in detail with a view to practical application. Their specific application to the hierarchical coding of three-dimensional (3-D) wire meshes is experimentally evaluated.

Optimal transform coding in the presence of quantization noise.

The optimal linear Karhunen-Loeve transform (KLT) attains the minimum reconstruction error for a fixed number of transform coefficients assuming that these coefficients do not contain noise. In any real coding system, however, the representation of the coefficients using a finite number of bits requires the presence of quantizers. We formulate the optimal linear transform using a data model that incorporates the quantization noise. Our solution does not correspond to an orthogonal transform and in fact, it achieves a smaller mean squared error (MSE) compared to the KLT, in the noisy case. Like the KLT, our solution depends on the statistics of the input signal, but it also depends on the bit-rate used for each coefficient. Especially for images, based on our optimality theory, we propose a simple modification of the discrete cosine transform (DCT). Our coding experiments show a peak signal-to noise ratio (SNR) performance improvement over JPEG of the order of 0.2 dB with an overhead less than 0.01 b/pixel.

A review of compression methods for medical images in PACS.

As an introduction to Section C2 (medical imaging) of track C (Images and PACS) of MIE '97, an appropriate and timely topic concerns the coding for the transmission of medical images in PACS. Speed limitations of existing networks along with the explosive growth of image modalities with extremely high volume outputs have combined to make the issue of medical image coding one of the key considerations in the design of future PACS systems. Both lossless and lossy compression schemes are reviewed and compared, and the compression demands are presented of the main digital medical image modalities.

An adaptive backpropagation neural network for real-time ischemia episodes detection: development and performance analysis using the European ST-T database.

A supervised neural network (NN)-based algorithm was used for automated detection of ischemic episodes resulting from ST segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular, the performance was measured in terms of beat-by-beat ischemia detection and in terms of the detection of ischemic episodes. The algorithm used to train the NN was an adaptive backpropagation (BP) algorithm. This algorithm drastically reduces training time (tenfold decrease in our case) when compared to the classical BP algorithm. The recall phase of the NN is then extremely fast, a fact that makes it appropriate for real-time detection of ischemic episodes. The resulting NN is capable of detecting ischemia independent of the lead used. It was found that the average ischemia episode detection sensitivity is 88.62% while the ischemia duration sensitivity is 72.22%. The results show that NN can be used in electrocardiogram (ECG) processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCU's).

Hippocrates: an integrated platform for telemedicine applications.

This paper describes 'Hippocrates', an integrated platform for telemedicine applications. Hippocrates allows computer supported co-operative work based on patient data folders consisting of selected diagnostic images, annotation text, patient history and other information. All data transferred is encrypted to ensure confidentiality and integrity. It operates on a local level over TCP/IP LAN environment and on a remote level over public ISDN lines.

Teleworks: a CSCW application for remote medical diagnosis support and teleconsultation.

The present paper describes methods for the design of both synchronous and asynchronous computer-supported cooperative work (CSCW) procedures suitable for the medical application area and specifically for the purpose of medical teleconsultation and remote diagnosis support. The experimental implementation of a CSCW system built upon a PC/Windows platform is detailed as an example of a low-cost system suitable for adoption in a wide range of medical teleconsultation applications.

Neural classifiers using one-time updating.

The linear threshold element (LTE), or perceptron, is a linear classifier with limited capabilities due to the problems arising when the input pattern set is linearly nonseparable. Assuming that the patterns are presented in a sequential fashion, we derive a theory for the detection of linear nonseparability as soon as it appears in the pattern set. This theory is based on the precise determination of the solution region in the weight space with the help of a special set of vectors. For this region, called the solution cone, we present a recursive computation procedure which allows immediate detection of nonseparability. The separability-violating patterns may be skipped so that, at the end, we derive a totally separable subset of the original pattern set along with its solution cone. The intriguing aspect of this algorithm is that it can be directly cast into a simple neural-network implementation. In this model the synaptic weights are committed (they are updated only once, and the only change that may happen after that is their destruction). This bears resemblance to the behavior of biological neural networks, and it is a feature unlike those of most other artificial neural techniques. Finally, by combining many such neural models we develop a learning procedure capable of separating convex classes.

Optimal construction of subband coders using Lloyd-Max quantizers.

A new method is presented for the analysis of the effects of Lloyd-Max quantization in subband filterbanks and for the optimal design of such filterbanks. A rigorous statistical model of a vector Lloyd-Max quantizer is established first, consisting of a linear time-invariant filter followed by additive noise uncorrelated/with the input. On the basis of this model, an expression for this variance of the error of a subband coder using Lloyd-Max quantizers is explicitly determined. Given analysis filters that statistically separate the subbands, it is shown that this variance is minimized if the synthesis filters are chosen, which mould achieve perfect reconstruction in lossless coding. The globally optimum of such a filterbank, minimizing the coder error variance, is further obtained by proper choice of its analysis filters. An alternative design method is also evaluated and optimized. In this, the errors correlated with the signal are set to zero, leaving a random error residue uncorrelated with the signal. This design method is optimized by choosing the analysis filters so as to minimize the random error variance. The results are evaluated experimentally in the realistic setting of a logarithmically split subband image coding scheme.

Use of nonlinear principal component analysis and vector quantization for image coding.

In the present work, the nonlinear principal component analysis (NLPCA) method is combined with vector quantization for the coding of images. The NLPCA is realized using the backpropagation neural network (NN), while vector quantization is performed using the learning vector quantizer (LVQ) NN. The effects of quantization in the quality of the reconstructed images are then compensated by using a novel codebook vector optimization procedure.

Design of CSCW applications for medical teleconsultation and remote diagnosis support.

Recent advances in telecommunication technology have permitted the implementation of tools for Computer Supported Cooperative Work (CSCW) based on real time transmission of different information types between two or more stations. The present paper describes methods for the design of both synchronous and asynchronous CSCW procedures suitable for the medical application area and specifically for the purpose of medical consultation and more generally, remote diagnosis support. The experimental implementation of such a CSCW system built upon a Personal Computer/Windows platform is detailed as an example of such a low-cost system suitable for adoption in a wide-range of medical teleconsultation applications.