Medical Image

In the pursuit of sensitive and quantitative methods to detect and diagnose cancer, nanotechnology has been identified as a field of great promise. Semiconductor quantum dots are nanoparticles with intense, stable fluorescence, and could enable the detection of tens to hundreds of cancer biomarkers in blood assays, on cancer tissue biopsies, or as contrast agents for medical imaging. With the emergence of gene and protein profiling and microarray technology, high-throughput screening of biomarkers has generated databases of genomic and expression data for certain cancer types, and has identified new cancer-specific markers. Quantum dots have the potential to expand this in vitro analysis, and extend it to cellular, tissue and whole-body multiplexed cancer biomarker imaging.

Automated blood vessel segmentation is an important issue for assessing retinal abnormalities and diagnoses of many diseases. The segmentation of vessels is complicated by huge variations in local contrast, particularly in case of the minor vessels. In this paper, we propose a new method of texture based vessel segmentation to overcome this problem. We use Gaussian and L * a * b * perceptually uniform color spaces with original RGB for texture feature extraction on retinal images. A bank of Gabor energy filters are used to analyze the texture features from which a feature vector is constructed for each pixel. The Fuzzy C-Means (FCM) clustering algorithm is used to classify the feature vectors into vessel or non-vessel based on the texture properties. From the FCM clustering output we attain the final output segmented image after a post processing step. We compare our method with hand-labeled ground truth segmentation of five images and achieve 84.37% sensitivity and 99.61% specificity.

This paper focuses on the problem of contour extraction in medical images and shows that this segmentation task can be viewed as a tracking problem. The influence of the speckle noise and of the high nonlinearities is overcome by Monte Carlo methods. An efficient multiple model Monte Carlo algorithm for progressive contour growing (tracking) is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood. A PDA procedure using the contour edge magnitude is applied to cope with the measurement origin uncertainty. The filter performance is studied by extracting contours from a number of real and simulated ultrasound medical images and very good accuracy is achieved.

This paper is submitting the idea of Computer Aided Design (CAD) software application in manipulation of digital x-ray images (DICOM). The study also discusses the concept of raster and vector images as DICOM images will be referred to as raster graphic and CAD as vector graphic. Vectorizing allows an image to be more flexible and can be manipulated so that more information can be loaded into it. As such, it is not impossible that vectorizing method can also be performed on medical images using CAD software such as AutoCAD, Solidworks and others. A DICOM image with DCM format is converted to JPEG format using Medweb software. Then, by using Image2CAD software, the x-ray image is converted to DXF format. The results showed that patient's x-ray image can be manipulated by using AutoCAD software. Study shows that CAD is not only used in the manufacturing field, but it can also be used in the medical field as well.

Terahertz radiation is increasingly being applied in new and evolving technologies applied in areas such as homeland security and medical imaging. Thus a timely assessment of the potential hazards and health effects of occupational and general population exposure to THz radiation is required. We applied continuous-wave (CW) 0.1 THz radiation (0.031 mW/ cm 2) to dividing lymphocytes for 1, 2 and 24 h and examined the changes in chromosome number of chromosomes 1, 10, 11 and 17 and changes in the replication timing of their centromeres using interphase fluorescence in situ hybridization (FISH). Chromosomes 11 and 17 were most vulnerable (about 30% increase in aneuploidy after 2 and 24 h of exposure), while chromosomes 1 and 10 were not affected. We observed changes in the asynchronous mode of replication of centromeres 11, 17 and 1 (by 40%) after 2 h of exposure and of all four centromeres after 24 h of exposure (by 50%). It is speculated that these effects are caused by radiation-induced lowfrequency collective vibrational modes of proteins and DNA. Our results demonstrate that exposure of lymphocytes in vitro to a low power density of 0.1 THz radiation induces genomic instability. These findings, if verified, may suggest that such exposure may result in an increased risk of cancer. ᭧ 2008 by Radiation Research Society METHODS Cultures Lymphocytes were isolated by UNI-SEP maxi (NovaMed Ltd., Jerusalem, Israel. Catalog no. U-10) from 20 ml of whole blood obtained by vacu

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The purpose of this paper is to present a survey of recent (published in 1993 or later) publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods. The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is based on either segmented points or surfaces, or on techniques endeavouring to use the full information content of the images involved. .nl) a Also formerly and popularly CAT, computed axial tomography. b Also referred to as NMR, nuclear magnetic resonance, spin imaging and various other names. c Also echo(graphy).

The presented method addresses the problem of multi-spectral image segmentation. Multiple images of different modalities are used to improve segmentation, as better tissue separation can be achieved in a higher dimensional space. We use a model which takes into account the physical process of the medical image formation. In particular the method addresses the problem of partial volumes of tissues that are present in a single voxel at tissue boundaries. The parameters of the multi-dimensional tissue model are iteratively adjusted using an Expectation Maximisation (EM) optimisation technique. Bayes theory is used to generate probability maps for each segmented tissue which estimates the most likely tissue volume fraction within each voxel as opposed to previous approaches which attempt to compute how likely a certain grey level would be generated by a particular tissue class.

Objective: despite recent advances in medical imaging, pre-operative evaluation of liver tumors, whether benign or malignant, is often lacking in accuracy and precision. With the development of surgical laparoscopy, the benefits of diagnostic laparoscopy have been combined with those of operative ultrasound. This article aims to describe the technique of laparoscopic ultrasound of the liver, and to define its applications

We propose a new hierarchical approach to resolution scalable lossless and near-lossless (NLS) compression. It combines the adaptability of DPCM schemes with new hierarchical oriented predictors to provide resolution scalability with better compression performances than the usual hierarchical interpolation predictor or the wavelet transform. Because the proposed hierarchical oriented prediction (HOP) is not really efficient on smooth images, we also

In this paper, we contribute to the development of context-aware operating rooms by introducing a novel approach to modeling and monitoring the workflow of surgical interventions. We first propose a new representation of interventions in terms of multidimensional time-series formed by synchronized signals acquired over time. We then introduce methods based on Dynamic Time Warping and Hidden Markov Models to analyze and process this data. This results in workflow models combining low-level signals with high-level information such as predefined phases, which can be used to detect actions and trigger an event. Two methods are presented to train these models, using either fully or partially labeled training surgeries. Results are given based on tool usage recordings from sixteen laparoscopic cholecystectomies performed by several surgeons.

Mutual Information (MI) has been extensively used as a similarity measure in image registration and motion estimation, and it is particularly robust for 3D multimodal medical image registration. However, MI estimators are known i) to have a high variance and ii) to be computationally costly. In order to overcome these drawbacks, we propose a new similarity measure based on an Edgeworth-based third order expansion of MI and named 3-EMI in the following. This kind of approximation is well known in signal processing, and especially in Independent Components Analysis (ICA), but its computation is easier since data can be prewhitened contrary to images in registration. The performance of affine and non-rigid registrations based on the 3-EMI metric is studied through computer results in the context of cardiac multislice computed tomography. In fact, an estimate of the 3-EMI metric using sample statistics is compared to a histogram-based estimate of the standard normalized MI, showing a better robustness of the 3-EMI measure with respect to the range of the searched deformation. In addition, in practice, one part of the floating image may be missing regarding the reference image. Computer results show that our approach is less sensitive to such a practical problem.

We discuss some theoretical models for vital signs monitoring by using a UWB radar. Focusing attention on the respiration and heartbeat signals, we show the impact of relevant parameters, like the sampling time interval, on the ability to extract the desired signal parameters from the waveforms elaborated at the receiver. The role of the UWB pulse shape is also highlighted. With reference to more refined methods based on signal correlation, we propose a variant that does not need the availability of a locally generated reference signal and achieves good resolution for the movement detection, while ensuring limited processing times.

In this paper, we propose a novel region-based active contour model for image segmentation. Our model incorporates the global and local information in the energy function, enabling efficient segmentation of images while accounting for intensity inhomogeneity. Another interesting property of the proposed model is its convexity, making it independent of the initial condition and hence ideal for an automatic segmentation. Furthermore, the energy function of the proposed model is minimized in a computationally efficient way by using the Chambolle method. Experimental results on natural and medical images demonstrate the performance of our model over the current state-of-the-art.

Coronary calcified plaque (CP) is both an important marker of atherosclerosis and major determinant of the success of coronary stenting. Intracoronary optical coherence tomography (OCT) with high spatial resolution can provide detailed volumetric characterization of CP. We present a semiautomatic method for segmentation and quantification of CP in OCT images. Following segmentation of the lumen, guide wire, and arterial wall, the CP was localized by edge detection and traced using a combined intensity and gradient-based level-set model. From the segmentation regions, quantification of the depth, area, angle fill fraction, and thickness of the CP was demonstrated. Validation by comparing the automatic results to expert manual segmentation of 106 in vivo images from eight patients showed an accuracy of 78 ± 9%. For a variety of CP measurements, the bias was insignificant (except for depth measurement) and the agreement was adequate when the CP has a clear outer border and no guide-wire overlap. These results suggest that the proposed method can be used for automated CP analysis in OCT, thereby facilitating our understanding of coronary artery calcification in the process of atherosclerosis and helping guide complex interventional strategies in coronary arteries with superficial calcification.

Transmission of compressed medical images is becoming a vital tool in telemedicine. Thus new methods are needed for efficient image compression. This study discovers the best design parameters for a data compression scheme applied to digital magnetic resonance (MR) images. The proposed technique aims at reducing the transmission cost while preserving the diagnostic information. By selecting the wavelet packet's filters, decomposition level, and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimal distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of MR images. In all cases, decomposition at three or four levels with the Coiflet 5 wavelet (Coif 5) results in better compression performance than the other wavelets. Image resolution is found to have a remarkable effect on the compression rate.

This report describes the design, implementation, evaluation and original enhancements to the Live-Wire method for 2D and 3D image segmentation. Live-Wire 2D employs a semi-automatic paradigm; the user is asked to select a few boundary points of the object to segment, to steer the process in the right direction, while the result is displayed in real time. In our implementation segmentation is extended to three dimensions by performing this process on a slice-by-slice basis. User's time and involvement is further reduced by allowing him to specify object contours in planes orthogonal to the slices. If these planes are chosen strategically, Live-Wire 3D can perform 2D segmentation in the plane of each slice automatically. This report also proposes two improvements to the original method, path heating and a new graph edge feature function based on variance of path properties along the boundary. We show that these improvements lead up to a 33% reduction in interaction with the user, and improved delineation in presence of strong interfering edges.

Information-theoretic notion of entropy (in Shannon sense) is a versatile avenue in analyzing bioinformatic details as well as in mining data pertinent to molecular biology. Also, the informatics of bio-and medical-imaging can be comprehended via entropy considerations. Presented in this paper is a comprehensive review on salient methods, newer techniques and open-questions on unexplored efforts vis-à-vis the entropy principles cast on bioinformatic computations and bio-/medicalimage informatics. Supplementing energetics algorithms are indicated

The early detection of breast cancer increases the survival rate in women. Today, film-screen mammography is the most common and effective technique for the detection of breast cancer. However, the film-screen image recording system of current mammography has several technical limitations that can reduce the breast cancer diagnostic accuracy. A state-of-theart technology, full-field direct digital mammography (FFDDM) has the potential to increase the sensitivity of clinical and screening examinations and emerge as a film-screen mammography replacement. It is believed that efficiently delivering the superior image quality of FFDDM to expert mammographers will significantly increase the breast cancer diagnostic accuracy. This concept motivates the studies of FFDDM telemammography.

This paper describes a novel extension of the photon mapping algorithm, capable of handling both volume multiple inelastic scattering and curved light paths simultaneously. The extension is based on the Full Radiative Transfer Equation (FRTE) and Fermat's law, and yields physically accurate, high-dynamic data than can be used for image generation or for other simulation purposes, such as driving simulators, underwater vision or lighting studies in architecture. Photons are traced into the participating medium with a varying index of refraction, and their curved trajectories followed (curved paths are the cause of certain atmospheric effects such as mirages or rippling desert images). Every time a photon is absorbed, a Russian roulette algorithm based on the quantum efficiency of the medium determines whether the inelastic scattering event takes place (causing volume fluorescence). The simulation of both underwater and atmospheric effects is shown, providing a global illumination solution without the restrictions of previous approaches.

A nuclear imaging of heart is a noninvasive procedure that reveals the cardiac structure and physiology enabling evaluation of cardiac function. According to the regulations published by the Polish Government and the Polish Ministry of Health, the nuclear medicine departments must follow the new legislation which has imposed quality assurance (QA) and quality control (QC) of equipment and medical testing procedures as compulsory.

A method for quantitative assessment of tree structures is reported allowing evaluation of airway tree morphology and its associated function. Our skeletonization and branch-point identification method provides a basis for tree quantification or tree matching, tree-branch diameter measurement in any orientation, and labeling individual branch segments. All main components of our method were specifically developed to deal with imaging artifacts typically present in volumetric medical image data. The proposed method has been tested in a computer phantom subjected to changes of its orientation as well as in repeatedly CT-scanned rigid and rubber plastic phantoms. In this paper, validation is reported in six in vivo scans of the human chest.

Rhinologists are often faced with the challenge of assessing nasal breathing from a functional point of view to derive effective therapeutic interventions. While the complex nasal anatomy can be revealed by visual inspection and medical imaging, only vague information is available regarding the nasal airflow itself: Rhinomanometry delivers rather unspecific integral information on the pressure gradient as well as on total flow and nasal flow resistance. In this article we demonstrate how the understanding of physiological nasal breathing can be improved by simulating and visually analyzing nasal airflow, based on an anatomically correct model of the upper human respiratory tract. In particular we demonstrate how various Information Visualization (InfoVis) techniques, such as a highly scalable implementation of parallel coordinates, time series visualizations, as well as unstructured grid multi-volume rendering, all integrated within a multiple linked views framework, can be utilized to gain a deeper understanding of nasal breathing. Evaluation is accomplished by visual exploration of spatio-temporal airflow characteristics that include not only information on flow features but also on accompanying quantities such as temperature and humidity. To our knowledge, this is the first in-depth visual exploration of the physiological function of the nose over several simulated breathing cycles under consideration of a complete model of the nasal airways, realistic boundary conditions, and all physically relevant time-varying quantities.

Large medical image datasets form a rich source of anatomical descriptions for research into pathology and clinical biomarkers. Many features may be extracted from data such as MR images to provide, through manifold learning methods, new representations of the population's anatomy. However, the ability of any individual feature to fully capture all aspects morphology is limited. We propose a framework for deriving a representation from multiple features or measures which can be chosen to suit the application and are processed using separate manifold-learning steps. The results are then combined to give a single set of embedding coordinates for the data. We illustrate the framework in a population study of neonatal brain MR images and show how consistent representations, correlating well with clinical data, are given by measures of shape and of appearance. These particular measures were chosen as the developing neonatal brain undergoes rapid changes in shape and MR appearance and were derived from extracted cortical surfaces, non-rigid deformations and image similarities. Combined single embeddings show improved correlations demonstrating their benefit for further studies such as identifying patterns in the trajectories of brain development. The results also suggest a lasting effect of age at birth on brain morphology, coinciding with previous clinical studies.

In the past two decades radiography has experienced a wealth of changes, involving the teaching site, learning methods, curriculum, professional status, educational funding, and public expectations. Consequently this period witnessed the transition of radiography from a mainly hospital-linked to a mainly university-linked degree, from a knowledge-based discipline to an evidence-based practice. The early 1990s saw the establishment of graduate programs, the role expansion of radiographers, the technological advancements in medical imaging, the participation of the Radiography Schools in Research Assessment Exercise (RAE) schemes.

For all p > 2, k > p, a reflection size-and-shape space SΣ k p,0 of k-ads in general position in R p , invariant under translation, rotation and reflection, is shown to be a smooth manifold and is equivariantly embedded in a space of symmetric matrices, allowing a nonparametric statistical analysis based on extrinsic means.

Watershed transformation is a common technique for image segmentation. However, its use for automatic medical image segmentation has been limited particularly due to oversegmentation and sensitivity to noise. Employing prior shape knowledge has demonstrated robust improvements to medical image segmentation algorithms. We propose a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge. Our method iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments. Quantitative validation of magnetic resonance imaging segmentation results supports the robust nature of our method.

To date, Gd (III) based contrast agents are commonly used in MR-medical imaging. In the last 15 years, our overall goal has been to develop targeting and responsive Gd (III) containing probes for innovative magnetic resonance imaging (MRI) applications. ...

Many recent high angular resolution diffusion imaging (HARDI) reconstruction techniques have been introduced to infer an orientation distribution function (ODF) of the underlying tissue structure. These methods are more often based on a single-shell (one b-value) acquisition and can only recover angular structure information contained in the ensemble average propagator (EAP) describing the three-dimensional (3D) average diffusion process of water molecules. The EAP can thus provide richer information about complex tissue microstructure properties than the ODF by also considering the radial part of the diffusion signal. In this paper, we present a novel technique for analytical EAP reconstruction from multiple q-shell acquisitions. The solution is based on a Laplace equation by part estimation between the diffusion signal for each shell acquisition. This simplifies greatly the Fourier integral relating diffusion signal and EAP, which leads to an analytical, linear and compact EAP reconstruction. An important part of the paper is dedicated to validate the diffusion signal estimation and EAP reconstruction on real datasets from ex vivo phantoms. We also illustrate multiple q-shell diffusion propagator imaging (mq-DPI) on a real in vivo human brain and perform a qualitative comparison against state-of-the-art diffusion spectrum imaging (DSI) on the same subject. mq-DPI is shown to reconstruct robust EAP from only several different b-value shells and less diffusion measurements than DSI. This opens interesting perspectives for new q-space sampling schemes and tissue microstructure investigation.

In this work we propose a comprehensive study of Digital Stent Enhancement (DSE), from the analysis of requirements to the validation of the proposed solution. First, we derive the stent visualization requirements in the context of the clinical application and workflow. Then, we propose a DSE algorithm combining automatic detection, tracking, registration and contrast enhancement. The most original parts of our solution: landmark segmentation and non-linear image registration are detailed. Finally, we validate the algorithm on a large number of synthetic and clinical cases. Performance is characterized in terms of image quality, automation, and execution time. This work is, to the best of our knowledge, the first comprehensive article on DSE, covering problem statement, proposed solution, and validation strategies.

The impact of image pattern recognition on accessing large databases of medical images has recently been explored, and content-based image retrieval (CBIR) in medical applications (IRMA) is researched. At the present, however, the impact of image retrieval on diagnosis is limited, and practical applications are scarce. One reason is the lack of suitable mechanisms for query refinement, in particular, the ability to (1) restore previous session states, (2) combine individual queries by Boolean operators, and (3) provide continuous-valued query refinement. This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement. The various mechanisms of manYmachine interaction during a retrieval session are grouped into four classes: (1) output modules, (2) parameter modules, (3) transaction modules, and (4) process modules, all of which are controlled by a detailed query logging. The query logging is linked to a relational database. Nes...

Resumo -Neste trabalho é apresentada a implantação de um servidor de imagens médicas com a implementação e integração de módulos para recuperação textual e baseada em conteúdo para o Serviço de Radiodiagnóstico de um Hospital Universitário. O sistema permite a aquisição, gerenciamento, armazenamento e disponibilização das informações dos pacientes, seus exames, laudos e imagens através da internet. Os exames radiológicos e suas respectivas imagens podem ser recuperados por informações textuais ou por similaridade do conteúdo pictórico das imagens. As imagens utilizadas são de ressonância magnética nuclear e tomografia computadorizada e são geradas no padrão DICOM 3.0. O sistema foi desenvolvido contemplando tecnologias para Web com interfaces amigáveis para recuperação das informações. Ele é composto por três módulos integrados sendo o servidor de imagens, o módulo de consulta textual e o módulo de consulta por similaridade. Os resultados apresentados indicam que as imagens são gerenciadas e armazenadas corretamente, bem como o tempo de retorno das imagens é clinicamente satisfatório, tanto para a consulta textual como para a consulta por similaridade. As avaliações da recuperação por similaridade apresentam que o extrator escolhido pode ser considerado relevante para separar as imagens por região anatômica.

Silicon microstrip detectors in the edge on geometry, i.e. oriented with the strips parallel to the incoming beam, allow a high absorption efficiency for X-rays in the 10–100 keV energy range. In medical imaging, this results in a significant reduction of the dose given to the patient with respect to conventional screen-film systems. This geometrical configuration was first proposed in the early 90s and since then it has been applied to several medical imaging projects. In this short review, an overview of some medical imaging applications using edge on detectors will be presented, focusing the attention on the main characteristics of the detection systems. The studies performed in Trieste in order to optimize such detectors for digital mammography with synchrotron radiation will be described in detail. In particular, novel imaging techniques (phase contrast, refraction and scatter imaging, computed tomography) implemented and tested at the Trieste synchrotron light source will be shortly discussed.

Hemodynamic factors are thought to be implicated in the progression and rupture of intracranial aneurysms. Current efforts aim to study the possible associations of hemodynamic characteristics such as complexity and stability of intra-aneurysmal flow patterns, size and location of the region of flow impingement with the clinical history of aneurysmal rupture. However, there are no reliable methods for measuring blood flow patterns in vivo. In this paper, an efficient methodology for patient-specific modeling and characterization of the hemodynamics in cerebral aneurysms from medical images is described. A sensitivity analysis of the hemodynamic characteristics with respect to variations of several variables over the expected physiologic range of conditions is also presented. This sensitivity analysis shows that although changes in the velocity fields can be observed, the characterization of the intra-aneurysmal flow patterns is not altered when the mean input flow, the flow division, the viscosity model, or mesh resolution are changed. It was also found that the variable that has the greater impact on the computed flow fields is the geometry of the vascular structures. We conclude that with the proposed modeling pipeline clinical studies involving large numbers cerebral aneurysms are feasible.

Resumo -Com a utilização de sistemas digitais para realização dos laudos radiológicos, tornou-se necessário criar ambientes de alta disponibilidade para garantir a continuidade do atendimento sem interrupções, aos pacientes em centros de pronto atendimento. Assim, é proposta uma arquitetura implantada no Hospital São Lucas da PUCRS que busca atender esta necessidade. O gerenciamento das imagens médicas adquiridas em um hospital envolve o seu armazenamento, transmissão, recuperação e integração com modalidades de geração de imagens e estações de visualização distribuídas. Para esse ambiente são necessários sistemas de gerenciamento de imagens juntamente com um banco de dados com boa performance. O grande volume de imagens geradas em um hospital agregado com o tamanho médio de uma imagem médica digital torna-se necessário um ambiente distribuído e com alta disponibilidade. Outra função importante de um sistema PACS é o roteamento de imagens que permite distribuir as imagens aos diversos setores do hospital. Neste artigo é apresentado um modelo personalizável de distribuição de imagens com possibilidade de aplicar filtros e conversões nas imagens antes de serem enviadas.

Computational models of human anatomy are mathematical representations of human anatomy designed to be used in dosimetry calculations. They have been used in dosimetry calculations for radiography, radiotherapy, nuclear medicine, radiation protection and to investigate the effects of low frequency electromagnetic fields. Tomographic medical imaging techniques have allowed the construction of digital three-dimensional computational models based on the actual anatomy of individual humans. These are called voxel models, tomographic models or phantoms. Their usefulness lies in their faithful representation of human anatomy and the flexibility they afford by being able to be scaled in size to match the required human dimensions. Segmenting medical images in order to make voxel models is very time-consuming so semi-automatic segmentation techniques are being developed. Some 21 whole or partial body models currently exist and more are being prepared. These models are listed and discussed.

This paper makes a review on the current segmentation algorithms used for medical images. Algorithms are divided into three categories according to their main ideas: the ones based on threshold, the ones based on pattern recognition techniques and the ones based on deformable models. The main tendency of each category with their principle ideas, application field, advantages and disadvantages are discussed. For each considered type some typical algorithms are described. Algorithms of the third category are mainly focused because of the intensive investigation on deformable models in the recent years. Possible applications of these algorithms on segmenting organs and tissues contained in the pelvic cavity are also discussed through several preliminary experiments.

The amount of digital medical images has increased rapidly in the Internet. The necessity of fast and secure diagnosis is vital in the medical world. Nowadays, the transmission of images became a daily routine and it is necessary to find an efficient form to transmit them over the net. In ...

We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike the original classifier, can be easily interpreted by humans. Each iteration of the rule extraction algorithm is formulated as a constrained optimization problem that is computationally inexpensive to solve. We discuss various properties of the algorithm and provide proof of convergence for two different optimization criteria We demonstrate the performance and the speed of the algorithm on linear classifiers learned from real-world datasets, including a medical dataset on detection of lung cancer from medical images. The ability to convert SVM's and other "black-box" classifiers into a set of human-understandable rules, is critical not only for physician acceptance, but also to reducing the regulatory barrier for medical-decision support systems based on such classifiers.

This paper addresses both theoretical issues in the analysis of extrinsic mean 3D reflection shapes and of mean 3D reflection size-and-shapes, as well as related computational aspects. Applications are in glaucomatous reflection shape change detection and in computation of reflection size-and-shapes of conformations of proteins.

Over the past four years there has been a tremendous resurgence of "public" interest inside and outside of IBM in APL and especially APL2 as a result of 1. an awareness of the importance of for technical computing, rapid prototyping, array algorithms for parallel computing, and advanced decision support 2. state-of-the-art work being done with APL in such diverse areas as medical imaging, finance, manufacturing scheduling, spot market oil buying and selling, computer design, chip design, mathematics, knowledge based systems statistics, etc. 3. IBM establishing APL2 as its strategic interactive language and making it a key player in its technical computing strategy Due to this renewed interest, I have compiled a list of some APL-related articles on Modeling and on Knowledge-Based Expert Systems .(KBES).