30,601 research outputs found

    Open-Source Telemedicine Platform for Wireless Medical Video Communication

    Get PDF
    An m-health system for real-time wireless communication of medical video based on open-source software is presented. The objective is to deliver a low-cost telemedicine platform which will allow for reliable remote diagnosis m-health applications such as emergency incidents, mass population screening, and medical education purposes. The performance of the proposed system is demonstrated using five atherosclerotic plaque ultrasound videos. The videos are encoded at the clinically acquired resolution, in addition to lower, QCIF, and CIF resolutions, at different bitrates, and four different encoding structures. Commercially available wireless local area network (WLAN) and 3.5G high-speed packet access (HSPA) wireless channels are used to validate the developed platform. Objective video quality assessment is based on PSNR ratings, following calibration using the variable frame delay (VFD) algorithm that removes temporal mismatch between original and received videos. Clinical evaluation is based on atherosclerotic plaque ultrasound video assessment protocol. Experimental results show that adequate diagnostic quality wireless medical video communications are realized using the designed telemedicine platform. HSPA cellular networks provide for ultrasound video transmission at the acquired resolution, while VFD algorithm utilization bridges objective and subjective ratings

    Medical imaging analysis with artificial neural networks

    Get PDF
    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

    Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements

    Get PDF
    This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or positioning of the vision sensors. It concentrates on the problem where images are encoded with compressed linear measurements. We propose a geometry-based correlation model in order to describe the common information in pairs of images. We assume that the constitutive components of natural images can be captured by visual features that undergo local transformations (e.g., translation) in different images. We first identify prominent visual features by computing a sparse approximation of a reference image with a dictionary of geometric basis functions. We then pose a regularized optimization problem to estimate the corresponding features in correlated images given by quantized linear measurements. The estimated features have to comply with the compressed information and to represent consistent transformation between images. The correlation model is given by the relative geometric transformations between corresponding features. We then propose an efficient joint decoding algorithm that estimates the compressed images such that they stay consistent with both the quantized measurements and the correlation model. Experimental results show that the proposed algorithm effectively estimates the correlation between images in multi-view datasets. In addition, the proposed algorithm provides effective decoding performance that compares advantageously to independent coding solutions as well as state-of-the-art distributed coding schemes based on disparity learning

    Emerging technologies for the non-invasive characterization of physical-mechanical properties of tablets

    Get PDF
    The density, porosity, breaking force, viscoelastic properties, and the presence or absence of any structural defects or irregularities are important physical-mechanical quality attributes of popular solid dosage forms like tablets. The irregularities associated with these attributes may influence the drug product functionality. Thus, an accurate and efficient characterization of these properties is critical for successful development and manufacturing of a robust tablets. These properties are mainly analyzed and monitored with traditional pharmacopeial and non-pharmacopeial methods. Such methods are associated with several challenges such as lack of spatial resolution, efficiency, or sample-sparing attributes. Recent advances in technology, design, instrumentation, and software have led to the emergence of newer techniques for non-invasive characterization of physical-mechanical properties of tablets. These techniques include near infrared spectroscopy, Raman spectroscopy, X-ray microtomography, nuclear magnetic resonance (NMR) imaging, terahertz pulsed imaging, laser-induced breakdown spectroscopy, and various acoustic- and thermal-based techniques. Such state-of-the-art techniques are currently applied at various stages of development and manufacturing of tablets at industrial scale. Each technique has specific advantages or challenges with respect to operational efficiency and cost, compared to traditional analytical methods. Currently, most of these techniques are used as secondary analytical tools to support the traditional methods in characterizing or monitoring tablet quality attributes. Therefore, further development in the instrumentation and software, and studies on the applications are necessary for their adoption in routine analysis and monitoring of tablet physical-mechanical properties

    Towards 3D printed multifunctional immobilization for proton therapy: initial materials characterization

    Get PDF
    Purpose: 3D printing technology is investigated for the purpose of patient immobilization during proton therapy. It potentially enables a merge of patient immobilization, bolus range shifting, and other functions into one single patient-speci c structure. In this rst step, a set of 3D printed materials is characterized in detail, in terms of structural and radiological properties, elemental composition, directional dependence, and structural changes induced by radiation damage. These data will serve as inputs for the design of 3D printed immobilization structure prototypes. Methods: Using four di erent 3D printing techniques, in total eight materials were subjected to testing. Samples with a nominal dimension of 20×20×80 mm3 were 3D printed. The geometrical printing accuracy of each test sample was measured with a dial gage. To assess the mechanical response of the samples, standardized compression tests were performed to determine the Young’s modulus. To investigate the e ect of radiation on the mechanical response, the mechanical tests were performed both prior and after the administration of clinically relevant dose levels (70 Gy), multiplied with a safety factor of 1.4. Dual energy computed tomography (DECT) methods were used to calculate the relative electron density to water ρe, the e ective atomic number Ze , and the proton stopping power ratio (SPR) to water SPR. In order to validate the DECT based calculation of radiological properties, beam measurements were performed on the 3D printed samples as well. Photon irradiations were performed to measure the photon linear attenuation coe cients, while proton irradiations were performed to measure the proton range shift of the samples. The direc- tional dependence of these properties was investigated by performing the irradiations for di erent orientations of the samples. Results: The printed test objects showed reduced geometric printing accuracy for 2 materials (deviation > 0.25 mm). Compression tests yielded Young’s moduli ranging from 0.6 to 2940 MPa. No deterioration in the mechanical response was observed after exposure of the samples to 100 Gy in a therapeutic MV photon beam. The DECT-based characterization yielded Ze ranging from 5.91 to 10.43. The SPR and ρe both ranged from 0.6 to 1.22. The measured photon attenuation coe cients at clinical energies scaled linearly with ρe. Good agreement was seen between the DECT estimated SPR and the measured range shift, except for the higher Ze . As opposed to the photon attenuation, the proton range shifting appeared to be printing orientation dependent for certain materials. Conclusions: In this study, the rst step toward 3D printed, multifunctional immobilization was performed, by going through a candidate clinical work ow for the rst time: from the material printing to DECT characterization with a veri cation through beam measurements. Besides a proof of concept for beam modi cation, the mechanical response of printed materials was also investigated to assess their capabilities for positioning functionality. For the studied set of printing techniques and materials, a wide variety of mechanical and radiological properties can be selected from for the intended purpose. Moreover the elaborated hybrid DECT methods aid in performing in-house quality assurance of 3D printed components, as these methods enable the estimation of the radiological properties relevant for use in radiation therapy

    Detection of internal quality in kiwi with time-domain diffuse reflectance spectroscopy

    Get PDF
    Time-domain diffuse reflectance spectroscopy (TRS), a medical sensing technique, was used to evaluate internal kiwi fruit quality. The application of this pulsed laser spectroscopic technique was studied as a new, possible non-destructive, method to detect optically different quality parameters: firmness, sugar content, and acidity. The main difference with other spectroscopic techniques is that TRS estimates separately and at the same time absorbed light and scattering inside the sample, at each wavelength, allowing simultaneous estimations of firmness and chemical contents. Standard tests (flesh puncture, compression with ball, .Brix, total acidity, skin color) have been used as references to build estimative models, using a multivariate statistical approach. Classification functions of the fruits into three groups achieved a performance of 75% correctly classified fruits for firmness, 60% for sugar content, and 97% for acidity. Results demonstrate good potential for this technique to be used in the development of new sensors for non-destructive quality assessment

    ART Neural Networks for Remote Sensing Image Analysis

    Full text link
    ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing Company, medical database prediction, and robot vision. This paper features a self-contained introduction to ART and ARTMAP dynamics. An application of these networks to image processing is illustrated by means of a remote sensing example. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, which allows the network to encode important rare cases but which may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. Recently developed ART models (dART and dARTMAP) retain stable coding, recognition, and prediction, but allow arbitrarily distributed category representation during learning as well as performance
    corecore