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In low- and middle-income regions, a relatively large number of deaths occur from cardiovascular disease or stroke. Carotid intima–media thickness (cIMT) and carotid lumen diameter (cLD) are strong indicators of cardiovascular event risk and stenosis severity, respectively. The interactive open-source software described here, Cimtool, is based on active contours for measuring these indicators in clinical practice and thus helping in preventive diagnosis and treatment. Cimtool was validated using carotid phantoms and real images obtained using ultrasound. Expert users measured cIMT and cLD in regular practice and also with Cimtool. The results obtained with Cimtool were then compared with the results for the manual approach in terms of measurement agreement, time spent on the measurements and usability. Intra-observer variability when using Cimtool was also analyzed. Statistical analysis revealed strong agreement between the manual method and Cimtool (p > 0.01 for cIMT and cLD). The correlation coefficient for both cIMT and cLD measurements was r > 0.9. Moreover, this software allowed the users to spend considerably less time on each measurement (3.5 min per study versus 50 s with Cimtool on average). An open-source, interactive, validated tool for measuring cIMT and cLD clinically was thus developed. Compared with the manual approach, Cimtool's straightforward measurement flow allows the user to spend less time per measurement and has less standard deviation. The coefficients of variation for measurements and intra-observer variability were lower than those reported for recent automated approaches, even with low-quality images.

Tensor voting is a computational framework that addresses the problem of perceptual organisation. It was designed to convey human perception principles into a unified framework that can be adapted to extract visually salient elements from possibly noisy or corrupted images. The original formulation featured some concerns that made it difficult or impractical to be applied directly. Therefore, several partial or total theoretical reformulations or augmentations have been proposed. These almost parallel publication were presented in different directions, with different priorities and even in a different notation. Thus, the authors observed the need for a coherent description and comparison of the different proposals. This work, after describing the original approach of tensor voting, reviews and explains a number of high impact theoretical modifications in a self-contained manner and including possible future directions of work. The authors have selected and organised a number of formulations and unified the way the problem is addressed across the different proposals. The aim of this study is to contribute with a modern comprehensive source of information on the theoretical aspects of tensor voting.

Ultrasound is widely used as an inexpensive, real-time method for imaging vascular tissue. However, sonographs often lack automatic or semi-automatic software for measuring vascular diameter precisely, especially in low- and mid-income countries or institutions. Tools can be developed to perform this task, but they must be validated before being accepted for clinic use. For that purpose, in this work we present low-cost phantoms that resemble vascular tissue when subjected to ultrasound. Several materials are analysed and a step-by-step recipe for building a simple phantom is presented. Qualitatively, models were imaged by an ultrasound expert physician, and several characteristic are assessed. Quantitatively, a comparison between ultrasound and caliper measurements of the phantoms is presented. Finally, a discussion about the results and the recommended materials for low-cost vascular phantoms is carried out.

© 2017 SPIE. Most current systems for automated glaucoma detection in fundus images rely on segmentation-based features, which are known to be in uenced by the underlying segmentation methods. Convolutional Neural Networks (CNNs) are powerful tools for solving image classification tasks as they are able to learn highly discriminative features from raw pixel intensities. However, their applicability to medical image analysis is limited by the non-availability of large sets of annotated data required for training. In this article we present results of analysis of the viability of using CNNs that are pre-trained from non-medical data for automated glaucoma detection. Two different CNNs, namely OverFeat and VGG-S, were applied to fundus images to generate feature vectors. Preprocessing techniques such as vessel inpainting, contrast-limited adaptive histogram equalization (CLAHE) or cropping around the optic nerve head (ONH) area were explored within this framework to evaluate the improvement in feature discrimination, combined with both ℓ1 and ℓ2 regularized logistic regression models. Results on the Drishti-GS1 dataset, evaluated in terms of area under the average ROC curve, suggests the viability of this approach and offer significant evidence of the importance of well-chosen image pre-processing for transfer learning when the amount of data is not sufficient for fine-tuning the network. Orlando J.I., Prokofyeva E., del Fresnob M., Blaschko M., ''Convolutional neural network transfer for automated glaucoma identification'', Proceedings SPIE, vol. 10160, 12th international symposium on medical information processing and analysis - SIPAIM 2016, 10 pp., December 5-7, 2016, Tandil, Argentina. ispartof: pages:1-10 ispartof: Proceedings SPIE vol:10160 pages:1-10 ispartof: International symposium on medical information processing and analysis - SIPAIM 2016 location:Tandil, Argentina date:5 Dec - 7 Dec 2016 status: published

Background: Intravascular ultrasound (IVUS) provides axial greyscale images, allowing the assessment of the vessel wall and the surrounding tissues. Several studies have described automatic segmentation of the luminal boundary and the media–adventitia interface by means of different image features. Purpose: The aim of the present study is to evaluate the capability of some of the most relevant state-of-the-art image features for segmenting IVUS images. The study is focused on Volcano 20 MHz frames not containing plaque or containing fibrotic plaques, and, in principle, it could not be applied to frames containing shadows, calcified plaques, bifurcations and side vessels. Methods: Several image filters, textural descriptors, edge detectors, noise and spatial measures were taken into account. The assessment is based on classification techniques previously used for IVUS segmentation, assigning to each pixel a continuous likelihood value obtained using support vector machines (SVMs). To retrieve relevant features, sequential feature selection was performed guided by the area under the precision–recall curve (AUC-PR). Results: Subsets of relevant image features for lumen, plaque and surrounding tissues characterization were obtained, and SVMs trained with these features were able to accurately identify those regions. The experimental results were evaluated with respect to ground truth segmentations from a publicly available dataset, reaching values of AUC-PR up to 0.97 and Jaccard index close to 0.85. Conclusion: Noise-reduction filters and Haralick’s textural features denoted their relevance to identify lumen and background. Laws’ textural features, local binary patterns, Gabor filters and edge detectors had less relevance in the selection process. Fil: Lo Vercio, Lucas. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Orlando, José Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: del Fresno, Mirta Mariana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Larrabide, Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed framework has a wide range of applications especially in the presence of adjacent structures of interest or under intra-structure inhomogeneities giving excellent quantitative results.

Using Web-PACS has become an attractive option that brings access to medical imaging databases from remote hosts. However, the big size of the medical images to be transmitted impacts negatively on the teleradiologist experience by increasing the dead times elapsed between study request and visualization on the screen. In this context, it is of the upmost interest to implement strategies to optimize image transmission. In this work a system (Dcm-Ar) for remote visualization of DICOM files is presented, which strongly reduces the time-to-display (TTD), thus improving the teleradiologist comfort. Dcm-Ar is made up of a Server interacting with a PACS and a remote Client. The Client is a DICOM viewer, which is based on the Adobe® Flash™ virtual machine. This fact ensures easy and costless dissemination of the technology.