Biomedical Imaging

Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. Each modality records brain structure and function at different scales, and fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. Recently, a number of methods have been proposed for data integration and fusion of two brain imaging modalities. We propose a new data fusion scheme based on canonical correlation analysis that enables the detection of associations across multiple modalities. Our multimodal canonical correlation analysis (mCCA) scheme works at the feature level using multi-set CCA to determine inter-subject covariations across modalities. We apply mCCA to fMRI, sMRI, and EEG data collected from patients diagnosed with schizophrenia and healthy controls. Through data collected from an auditory oddball task, we show that the fusion of multiple modalities detects more specific associations as compared to fusion of two modalities.

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

Voxelation allows high-throughput acquisition of multiple volumetric images of brain gene expression, similar to those obtained from biomedical imaging systems. To obtain these images, the method employs analysis of spatially registered voxels (cubes). For creation of high-resolution maps using voxelation, relatively small voxel sizes are necessary and instruments will be required for semiautomated harvesting of such voxels. Here, we describe two devices that allow spatially registered harvesting of voxels from the human and rodent brain, giving linear resolutions of 3.3 and 1 mm, respectively. Gene expression patterns obtained using these devices showed good agreement with known expression patterns. The voxelation instruments and their future iterations represent a valuable approach to the genome scale acquisition of gene expression patterns in the human and rodent brain. #

Previous studies have documented the application of administrative claim dataset for health services research purposes. In addition to administrative and billing details of healthcare services, insurance claim datasets can reveal important information regarding professional interactions or links that evolve among healthcare service providers through, for example, informal knowledge sharing. The aim of this study is to develop a research framework, which uses details of such professional interactions, to learn about effective healthcare coordination and collaboration. The proposed framework has been exercised to analyse Patient-centric Care Coordination Network and Physician Collaboration Network. The usefulness of this framework and its applications in exploring different collaborative efforts of healthcare service providers have been discussed in this paper.

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.

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

Example question: Assume you have a transducer made of a PZT with an acoustic impedance of 29 MRayls. If the transducer is directly coupled to soft tissue with an acoustic impedance of 1.6 MRayls, what fraction of the intensity is ​ transmitted​ into the tissue.

Although in Western countries there are hardly any radiologists using PACS in their daily routine, it is clear that the future is digital. Clinical radiological experiences with PACS generated many unsolved questions, such as: What is the impact of PACS on the reading of radiological examinations?. After years of centralization, will radiology again decentralize by the possibilities of PACS? Who is responsible for the radiological information? What has to be done by radiologists to keep control of radiology? This paper will discuss these questions in correlation with the different phases of the radiological imaging process. PACS will be one of the main changes which will influence radiology in the future. Radiologists are still in the position to play an important role in future health care; therefore, an active participation in defining the future requirements is needed now.

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

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.

In radionuclide therapy, absorbed dose is calculated by convolution of a three-dimensional activity matrix with a three-dimensional dose point kernel. A technique employing the fast Hartley Transform (FHT) has been developed to perform this calculation. An important part of that development was the indexing scheme for 3D data. The results of this new FHT convolution technique were compared to direct convolution. A cube was convolved with itself by these two techniques. The results differed by less than 2 percent. In an effort to show the practical applicability of 3D convolution, a three-dimensional activity matrix from a I-131-labeled 16.88 monoclonal antibody patient was convolved with beta and photon dose point kernels using direct convolution. Isodose contours were then generated from the calculated absorbed dose matrix and overlaid on a CT image of the patient

Retinal blood vessel assessment plays an important role in the diagnosis of ophthalmic pathologies. The use of digital images for this purpose enables the application of a computerized approach and has fostered the development of multiple methods for automated vascular tree segmentation. Metrics based on contingency tables for binary classification have been widely used for evaluating the performance of these algorithms. Metrics from this family are based on the measurement of a success or failure rate in the detected pixels, obtained by means of pixel-to-pixel comparison between the automated segmentation and a manually-labeled reference image. Therefore, vessel pixels are not considered as a part of a vascular structure with specific features. This paper contributes a function for the evaluation of global quality in retinal vessel segmentations. This function is based on the characterization of vascular structures as connected segments with measurable area and length. Thus, its design is meant to be sensitive to anatomical vascularity features. Comparison of results between the proposed function and other general quality evaluation functions shows that this proposal renders a high matching degree with human quality perception. Therefore, it can be used to enhance quality evaluation in retinal vessel segmentations, supplementing the existing functions. On the other hand, from a general point of view, the applied concept of measuring descriptive properties may be used to design specialized functions aimed at segmentation quality evaluation in other complex structures.

This paper presents a methodology and algorithm for generating diffeomorphisms of the sphere onto itself, given the displacements of a finite set of template landmarks. Deformation maps are constructed by integration of velocity fields that minimize a quadratic smoothness energy under the specified landmark constraints. We present additional formulations of this problem which incorporate a given error variance in the positions of the landmarks. Finally, some experimental results are presented. This work has application in brain mapping, where surface data is typically mapped to the sphere as a common coordinate system.

The minimum variance (MV) beamformer, also known as the Capon or minimum variance distortionless response (MVDR) beamformer, uses the recorded wavefield to compute a set of optimal weights to be applied to each sensor, before coherently adding the sensor outputs. The weights are chosen such that the variance of the output is minimized while maintaining unit gain in the view direction. The MV beamformer offers improved resolution and image quality compared to the conventional delay-and-sum (DAS) beamformer. The MV beamformer was originally introduced for passive systems. When adapting the MV beamformer to active sonar, sub-array averaging is necessary in order to avoid signal cancellation of coherent signals, and to reduce the sensitivity to errors in the wavefield parameters. Sub-array averaging is a technique developed for flat, uniformly sampled arrays, and it is not immediately evident that this will give satisfactory results on a non-flat array. In this work, we have successfully implemented the MV beamformer on an active 1D circular sonar array suitable for fishery sonar. We demonstrate, through simulations, that it is feasible to apply the MV beamformer with sub-array averaging to a circular array. Our results have been verified using experimental data from the Simrad SX90 fishery sonar.

A new portable brain imaging device based on continuous-wave functional near-infrared spectrometry (fNIRS) is presented. The source-detector part is composed of a multi-wavelength LED and a silicon photodetector that are directly placed on the scalp of the subject. The dimensions of the proposed device are small, as it has to be mounted on the head of an adult person. Acquired data are transmitted in real-time to a laptop for post processing using Matlab. Timemultiplexed light is used to achieve a higher SNR while keeping the device safe for long-term wearing. Preliminary evaluation on adults gave the expected accuracy and compare well with fNIRS characteristics found in literature, that are collected from bulky equipment. With a noise figure of -47dB and a sampling rate of 23Hz, the presented device is appropriate to isolate hemodynamic variations, which are strongly related to local cerebral activity.

Consider the following scenario: Images of the left ventricle are acquired in short axis view. The individual that was scanned was able to hold perfectly still for 128 heart beats. Cardiac triggered data were collected at 30 msec intervals and the subject's R-R interval was 900 msec. The images that were acquired were acquired with a matrix of 128 x 128. You have been provided k-space data that is a cartoon of the scenario above. The k-space data represents a cube of data, 128 x 128 x 30 where each 128 x 128 matrix represents data synchronized to each phase in the cardiac cycle.

Tele-radiology is one of the most evolved areas of telemedicine. The DICOM standard (digital imaging and communications in medicine) is commonly used for this, but it is not sufficient in all aspects. The mechanism presented in this paper allows the dynamic assignment of an IP address and automatic configuration of devices connected over the network. When DICOM was written only

Medical applications of RF/microwaves are highlighted in this paper. The emphasis is placed on newer emerging diagnostic and therapeutic applications, such as microwave breast cancer detection, and treatment with localized high power used in ablation of the heart, and liver, benign prostate hypertrophy, angioplasty, and others. A very brief outline of biological effects of RF/microwaves and associated issues is given as background to the applications.

Abstruct-Digital ultrasonic imaging systems are critically dependent upon transducers and "front-end'' electronics, for the generation of insonifying pulses, the detection of reflected echoes, and the conversion of data to digital form. The impulse response, frequency and spatial characteristics of the transducer, and the electrical characteristics o f the analog-tedigital interface are important factors in image reconstruction and enhancement, on a par with signal processing techniques. Selected theoretical and practical aspects of front-end hardware, and their influence o n image quality arc reviewcd. The theoretical basis of transducer design is briefly rcviewcd, including methods for prediction of transducer performance, acoustic field distribution, and element configuration: the limitations of each method are carefully discussed. Next, the design of the interface electronics is examined, demonstrating the requirements for sampling rate, signal-to-noise ratio, dynamic range, impedance matching, etc. The design and construction of a phased array transducer system is used to illustrate the practical aspects o f transducer and interface design, including the trade-offs involved. Finally, future trends in ultrasonic transducer design are discussed, including new piezoelcctric materials (e.g., rare earth piezoceramics and piezoelectric polymers and composites), as well as the impact of new integrated circuit techniques. I. INT1IOI)UCTION U LTRASONIC imaging for medical and nondestructive testing purposes has made significant progress since its very beginnings over thirty years ago [ 1 1. Much of the recent increase in the use of diagnostic ultrasonic imaging can be traced to improvements in image quality. The new digital imaging systems coming into use hold the promise of further enhancing image quality. In addition, new image analysis techniques are a key element in the "ultimate goal" of medical ultrasonic imaging, which is tissue characterization. The flexibility inherent in digital imaging systems enables the designer to use a wide range of algorithms to reconstruct, enhance, and analyze ultrasonic images. However, the usefulness of these algorithms is limited by the quality of the original data. Critical to any digital ultrasonic imaging system is the "front-end": the transducer and associated electronics which link the digital system to the medium under examination. Although imaging capabilities can be augmented by digital processing techniques, the front-end hardware often sets the system performance limits. In particular, the signal-to-noise ratio (SNR), bandwidth, and dynamic range of the raw digital data all have an influence on the utility of certain image processing approaches.

It has been widely accepted that for brain MR images, both the image density inhomogeneity (slowly-varying intensity changes across the field of view) and partial-volume effect (PVE) (more than one tissue type present in a single voxel) considerably reduce the accuracy and reliability of quantitative analysis for various clinical purposes. This paper presents a unified expectation-maximization (EM) approach, where PVE and intensity inhomogeneity are combined together into a builtin-one statistical model in additive and multiplicative formats. It assumes that each tissue type follows a conditionally-independent normal distribution, based on which the summation of all tissue contributions multiplied or added by the bias term leads to mean density value at each voxel. Meanwhile, the summation of all the tissue mixtures, which is unobservable but could be estimated via EM framework (many-to-one mapping), multiplied or added by the bias term would lead to the observed image density at each voxel. In doing so, both the inhomogeneity and tissue mixtures are updated voxel-by-voxel until the convergence of a stable solution. Comprehensive tests on simulated brain MR images strongly demonstrated the feasibilities of additive/multiplicative bias models and the effectiveness of the unified EM approach. In addition, additive and multiplicative bias field models reflect advantages in terms of stability and robustness.

A new algorithm for medical image retrieval is presented in the paper. An 8-bit grayscale image is divided into eight binary bit-planes, and then binary wavelet transform (BWT) which is similar to the lifting scheme in real wavelet transform (RWT) is performed on each bitplane to extract the multi-resolution binary images. The local binary pattern (LBP) features are extracted from the resultant BWT sub-bands. Three experiments have been carried out for proving the effectiveness of the proposed algorithm. Out of which two are meant for medical image retrieval and one for face retrieval. It is further mentioned that the database considered for three experiments are OASIS magnetic resonance imaging (MRI) database, NEMA computer tomography (CT) database and PolyU-NIRFD face database. The results after investigation shows a significant improvement in terms of their evaluation measures as compared to LBP and LBP with Gabor transform.

A flexible needle can be accurately steered by robotically controlling the orientation of the bevel tip as the needle is inserted into tissue. Here, we demonstrate the significant effect of friction between the long, flexible needle shaft and the tissue, which can cause a significant discrepancy between the orientation of the needle tip and the orientation of the base where the needle is controlled. Our experiments show that several common phantom tissues used in needle steering experiments impart substantial frictional forces to the needle shaft, resulting in a lag of over 45° for a 10 cm insertion depth in some phantoms; clinical studies have reported torques large enough to could cause similar errors during needle insertions. Such angle discrepancies will result in poor performance or failure of path planners and image-guided controllers, since the needles used in percutaneous procedures are too small for state-of-the-art imaging to accurately measure the tip angle. To compensate...

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer potentially new insights and avenues on combining differential privacy with other effective dimension reduction techniques and secure multiparty computing to clearly define various privacy models.

Computer-aided detection (CAD) is increasingly used in clinical practice and for many applications a multitude of CAD systems have been developed. In practice, CAD systems have different strengths and weaknesses and it is therefore interesting to consider their combination. In this paper, we present generic methods to combine multiple CAD systems and investigate what kind of performance increase can be expected. Experimental results are presented using data from the ANODE09 and ROC09 online CAD challenges for the detection of pulmonary nodules in computed tomography scans and red lesions in retinal images, respectively. For both applications, combination results in a large and significant increase in performance when compared to the best individual CAD system.

The context of this work is related to the vertebra segmentation. The method we propose is based on the active shape model (ASM). An original approach taking advantage of the edge polygonal approximation was developed to locate the vertebra positions in a Xray image. Despite the fact that segmentation results show good efficiency, the time is a key variable that has always to be optimized in a medical context. Therefore, we present how vertebra extraction can efficiently be performed in exploiting the full computing power of parallel (GPU) and heterogeneous (multi-CPU/multi-GPU) architectures. We propose a parallel hybrid implementation of the most intensive steps enabling to boost performance. Experimentations have been conducted using a set of high-resolution X-ray medical images, showing a global speedup ranging from 3 to 22, by comparison with the CPU implementation. Data transfer times between CPU and GPU memories were included in the execution times of our proposed implementation.

Sterotactic percutaneous lumpectomy is becoming an effective means of diagnosing and excising a small breast cancer. Radiation to the biopsy cavity by a brachytherapy balloon is an effective and minimally invasive method used to prevent recurrence of the cancer. Following percutaneous lumpectomy an elliptical shaped cavity is left in the breast. There is a period of up to four days prior to initiation of Brachytherapy while the tissue sample is undergoing pathology testing. If a small cancer is present and excised with clear margins Brachytherapy can be initiated. Currently there are no devices used to keep the cavity and track open between the time of percutaneous lumpectomy and the start of radiation therapy. Typically three invasive procedures are required, first the breast biopsy, second excision of the cancer and third balloon placement for future radiation treatments. The purpose of this project is to design a temporary cavity spacer and insertion method following percutaneous lumpectomy, allowing all three aspects of the procedure to be completed at the time of initial biopsy.

Diabetic retinopathy is one of the leading diseases affecting eyes. Lack of early detection and treatment can lead to total blindness of the diseased eyes. Recently, numerous researchers have attempted producing automatic diabetic retinopathy detection techniques to supplement diagnosis and early treatment of diabetic retinopathy symptoms. In this manuscript, a new approach has been proposed. The proposed approach utilizes the feature extracted from the fundus image using a local extrema information with quantized Haralick features. The quantized features encode not only the textural Haralick features but also exploit the multiresolution information of numerous symptoms in diabetic retinopathy. Long Short-Term Memory network together with local extrema pattern provides a probabilistic approach to analyze each segment of the image with higher precision which helps to suppress false positive occurrences. The proposed approach analyzes the retina vasculature and hard-exudate symptoms o...

This research work has application for attendance system of employer's and students in general. The system will facilitate institutions/ organization to make attendance individual in time along with data information thumb impression will be taken as a signature for the system entry. Main design and challenge in this system is the design of database architecture and its business logic.

The disorders identified with retina of the eye such as: Diabetic Retinopathy (DR), age-related Macular Degeneration (AMD), Glaucoma, etc. can cause visual impairments. The retinal fundus pictures of the patients can be acquired with a computerized fundus camera. It can then be used as machine learning application against the manual technique for the detection and prevention of DR. In this research, the retinal fundus image obtained from Himalaya Eye Hospital Pokhara, MESSIDOR database and EyePACS are used for the detection of DR along with its severity label. In the preprocessing phase, the images are first converted to grayscale images. Histogram equalization technique is performed to adjust image intensity in order to enhance contrast. Median filtering, a non-linear digital filtering technique, is implemented to remove noise present in the image. It too helps to improve the result for further processing. Blood vessels are detected and optic discs are identified and removed. Exuda...

In contrast to space-partitioning index structures, data-partitioning index structures naturally adapt to the actual data distribution which results in a very good query response behavior. Besides efficient query processing, modern database applications including computer-aided design, medical imaging, or molecular biology require fully-fledged database management systems in order to guarantee industrial-strength. In this paper, we show how we can achieve efficient query processing on data-partitioning index structures within general purpose database systems. We reduce the navigational index traversal cost by using "extended index range scans". If a directory node is "largely" covered by the actual query, the recursive tree traversal for this node can beneficially be replaced by a scan on the leaf level of the index instead of navigating through the directory any longer. On the other hand, for highly selective queries, the index is used as usual. In this paper, we demonstrate the benefits of this idea for spatial collision queries on the Relational R-tree. Our experiments with an Oracle9i database system show that our new approach outperforms common index structures and the sequential scan considerably.

In conventional planar detection structures, photon absorption efficiency is limited by the thickness of the detector, which is itself limited by charge transport properties in the chosen material. Therefore a trade-off must be found between photons absorption efficiency and charge collection efficiency. To overcome this compromise, alternative detector architecture has been proposed [1], where charge collection is realized perpendicularly to the photon absorption plane. The volume of the detector is micro-structured with an array of columnar electrodes (alternatively anodes and cathodes) which penetrate through the whole bulk. Thanks to this new 3D architecture of the detectors, and as long as electrodes are sufficiently close, various semiconductor materials can be considered for room temperature use.

The thought-translation device (TTD) consists of a training device and spelling program for the completely paralyzed using slow-cortical brain potentials (SCP). During the training phase, the self-regulation of SCPs is learned through visual-auditory feedback and positive reinforcement of SCPs; during the spelling phase, patients select letters or words with their SCPs. A psychophysiological system for detection of cognitive functioning in completely paralyzed patients is an integral part of the TTD. The neurophysiological and anatomical basis of SCP-regulation was investigated by recording of BOLD-response in functional magnetic resonance imaging. Results showed involvement of basal ganglia and premotor cortex for required SCP positivity. The clinical outcome of 11 paralyzed patients using the TTD and quality of life of severely paralyzed patients is described. First attempts to improve learning of brain regulation with transcranial magnetic stimulation were successful.

The physical basis for breast tumor detection with microwave imaging is the contrast in dielectric properties of normal and malignant breast tissues. Confocal microwave imaging involves illuminating the breast with an ultra-wideband pulse from a number of antenna locations, then synthetically focusing reflections from the breast. The detection of malignant tumors is achieved by the coherent addition of returns from these strongly scattering objects. In this paper, we demonstrate the feasibility of detecting and localizing small ( 1 cm) tumors in three dimensions with numerical models of two system configurations involving synthetic cylindrical and planar antenna arrays. Image formation algorithms are developed to enhance tumor responses and reduce early-and late-time clutter. The early-time clutter consists of the incident pulse and reflections from the skin, while the late-time clutter is primarily due to the heterogeneity of breast tissue. Successful detection of 6-mm-diameter spherical tumors is achieved with both planar and cylindrical systems, and similar performance measures are obtained. The influences of the synthetic array size and position relative to the tumor are also explored.

Smart textile technologies integrate computer functionality into textiles. Since a few years, smart clothing has been coming up in the sport and health sector and is increasingly implemented in everyday objects within private spaces. Especially the use of textiles for medical reasons and their potential use within Ambient Assisted Living-Concepts (AAL) make it necessary to understand users' perspectives on these technologies and the willingness to use them. Today, the understanding in which way individual attitudes and emotional and cognitive abilities, may impact the acceptance of pervasive health care technologies, is restricted. This research is focused on the users' hopes and fears towards smart clothing and examines perceived benefits and barriers. As women have a higher life expectancy and will dominate the group of old people in the future -gender was chosen as one central factor of interest. As the second factor we examined technical experience in order to learn if the acceptance for smart clothing is connected to the degree of users previous experience with technology. Outcomes revealed both factors --gender and technical experience --to be decisive factors for the acceptance of smart clothing. Generally, women and persons with low technical experience show considerable caveats towards the usage of smart clothing technologies what becomes most evident in the perceived barriers and fears connected to the usage of this new technology.

In contrast to space-partitioning index structures, data-partitioning index structures naturally adapt to the actual data distribution which results in a very good query response behavior. Besides efficient query processing, modern database applications including computer-aided design, medical imaging, or molecular biology require fully-fledged database management systems in order to guarantee industrial-strength. In this paper, we show how we can achieve efficient query processing on data-partitioning index structures within general purpose database systems. We reduce the navigational index traversal cost by using "extended index range scans". If a directory node is "largely" covered by the actual query, the recursive tree traversal for this node can beneficially be replaced by a scan on the leaf level of the index instead of navigating through the directory any longer. On the other hand, for highly selective queries, the index is used as usual. In this paper, we demonstrate the benefits of this idea for spatial collision queries on the Relational R-tree. Our experiments with an Oracle9i database system show that our new approach outperforms common index structures and the sequential scan considerably.

The purpose of this paper is to give an overview of the recent surgical intraoperational applications of indocyanine green fluorescence imaging methods, the basics of the technology, and instrumentation used. Well over 200 papers describing this technique in clinical setting are reviewed. In addition to the surgical applications, other recent medical applications of ICG are briefly examined.

Training is an effective way to acquire knowledge. In the last years, realistic training has been proposed and offered by the use of virtual reality (VR) systems. This work presents a VR based simulator for gynecologic exam training -the SITEG. This system integrates stereoscopic view and touch to simulate a real exam in a virtual environment. In this environment the user can practice and learn different gynecological pathologies in several stages.

Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called Adaptive Fuzzy-K-means (AFKM) clustering for image segmentation which could be applied on general images and/or specific images (i.e., medical and microscopic images), captured using different consumer electronic products namely, for example, the common digital cameras and CCD cameras. The algorithm employs the concepts of fuzziness and belongingness to provide a better and more adaptive clustering process as compared to several conventional clustering algorithms. Both qualitative and quantitative analyses favour the proposed AFKM algorithm in terms of providing a better segmentation performance for various types of images and various number of segmented regions. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods. 1 .

Objective: This paper focuses on the use of image-based machine learning techniques in medical image analysis. In particular, we present some variants of local binary patterns (LBP), which are widely considered the state of the art among texture descriptors. After we provide a detailed review of the literature about existing LBP variants and discuss the most salient approaches, along with their pros and cons, we report new experiments using several LBP-based descriptors and propose a set of novel texture descriptors for the representation of biomedical images. The standard LBP operator is defined as a grayscale invariant texture measure, derived from a general definition of texture in a local neighborhood. Our variants are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. These sets of features are then used for training a machine-learning classifier (a stand-alone support vector machine). Methods and materials: Extensive experiments are conducted using the following three datasets:

A panoramic cardiac imaging system consisting of three highspeed CCD cameras has been developed to image the surface electrophysiology of a rabbit heart via fluorescence imaging using a voltage-sensitive fluorescent dye. A robust, unique mechanical system was designed to accommodate the three cameras and to adapt to the requirements of future experiments. A unified computer interface was created for this application-a single workstation controls all three CCD cameras, illumination, stimulation, and a stepping motor that rotates the heart. The geometric reconstruction algorithms were adapted from a previous cardiac imaging system. We demonstrate the system by imaging a polymorphic cardiac tachycardia.