Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods

Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case

Deep Low-Frequency Radio Observations of the NOAO Bootes Field: I. Data Reduction and Catalog Construction

In this article we present deep, high-resolution radio interferometric observations at 153 MHz to complement the extensively studied NOAO Bootes field. We provide a description of the observations, data reduction and source catalog construction. From our single pointing GMRT observation of ~12 hours we obtain a high-resolution (26" x 22") image of ~11.3 square degrees, fully covering the Bootes field region and beyond. The image has a central noise level of ~1.0 mJy/beam, which rises to 2.0-2.5 mJy/beam at the field edge, placing it amongst the deepest ~150 MHz surveys to date. The catalog of 598 extracted sources is estimated to be ~92 percent complete for >10 mJy sources, while the estimated contamination with false detections is <1 percent. The low RMS position uncertainty of 1.24" facilitates accurate matching against catalogs at optical, infrared and other wavelengths. Differential source counts are determined down to <~10 mJy. There is no evidence for flattening of the counts towards lower flux densities as observed in deep radio surveys at higher frequencies, suggesting that our catalog is dominated by the classical radio-loud AGN population that explains the counts at higher flux densities. Combination with available deep 1.4 GHz observations yields an accurate determination of spectral indices for 417 sources down to the lowest 153 MHz flux densities, of which 16 have ultra-steep spectra with spectral indices below -1.3. We confirm that flattening of the median spectral index towards low flux densities also occurs at this frequency. The detection fraction of the radio sources in NIR Ks-band is found to drop with radio spectral index, which is in agreement with the known correlation between spectral index and redshift for brighter radio sources.Comment: 14 pages, 7 figures. Accepted for publication by A&A. Source catalog will be available from CDS soo

Delaunay triangulation based image enhancement for echocardiography images

A novel image enhancement approach for automatic echocardiography image processing is proposed. The main steps include undecimated wavelet based speckle noise reduction, edge detection, followed by a regional enhancement process that employs Delaunay triangulation based thresholding. The edge detection is performed using a fuzzy logic based center point detection and a subsequent radial search based fuzzy multiscale edge detection. The edges obtained are used as the vertices for Delaunay triangulation for enhancement purposes. This method enhances the heart wall region in the echo image. This technique is applied to both synthetic and real image sets that were obtained from a local hospital

Medical imaging analysis with artificial neural networks

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

A Detailed Investigation into Low-Level Feature Detection in Spectrogram Images

Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation—it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com

R&D Paths of Pixel Detectors for Vertex Tracking and Radiation Imaging

This report reviews current trends in the R&D of semiconductor pixellated sensors for vertex tracking and radiation imaging. It identifies requirements of future HEP experiments at colliders, needed technological breakthroughs and highlights the relation to radiation detection and imaging applications in other fields of science.Comment: 17 pages, 2 figures, submitted to the European Strategy Preparatory Grou