Abstract
Colorectal carcinoma is a major health concern worldwide and its high incidence and mortality require accurate screening methods. Following endoscopic examination, polyps must be removed for histopathological characterization. Aiming to contribute to the improvement of current endoscopy methods of colorectal carcinoma screening or even for future development of laser treatment procedures, we studied the diffusion properties of glucose and water in colorectal healthy and pathological mucosa. These parameters characterize the tissue dehydration and the refractive index matching mechanisms of optical clearing (OC). We used ex vivo tissues to measure the collimated transmittance spectra and thickness during treatments with OC solutions containing glucose in different concentrations. These time dependencies allowed for estimating the diffusion time and diffusion coefficient values of glucose and water in both types of tissues. The measured diffusion times for glucose in healthy and pathological mucosa samples were 299.2 +/- 4.7 s and 320.6 +/- 10.6 s for 40% and 35% glucose concentrations, respectively. Such a difference indicates a slower glucose diffusion in cancer tissues, which originate from their ability to trap far more glucose than healthy tissues. We have also found a higher free water content in cancerous tissue that is estimated as 64.4% instead of 59.4% for healthy mucosa. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

Abstract
A complete analysis of the vascular system is a complex task since a large number of parameters are involved. In the research herein reported we present a novel medical framework called web-based integration for vascular expert research networks (Wivern) to be used in a multi-clinical department environment for the analysis of micro and macrocirculation. This tool can manage clinical information of several specialties, such as Neurology or Ophthalmology, and provides computer-aided tools to automatically analyze retinographies, carotid ultrasounds and blood pressure monitor signals, and to automatically compute cardiovascular risk stratification. Wivern is a web-based application with a user friendly interface that provides cross-platform compatibility and device independence. Several automated procedures are integrated within the framework, as a service on the web, to extract relevant parameters from clinical data, physiological signals and medical images. The application is planned for collecting and analyzing data in several clinical studies in different hospital centers to test their behavior and practical use of the different tools of the platform. The usefulness and validation of the system was achieved after the inclusion, by the different medical units, of 800 patients to analyze their hypertensive profile. Moreover, 800 retinal images were processed as well as 400 carotid were analyzed. Wivern provides a unique opportunity for vascular research since it enables an interdisciplinary and integrated study of the vascular network, allowing a more comprehensive evaluation of the consequences of any abnormality. The application also includes automated methods to process patient data in order to simplify the physician tasks.

Abstract
In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710 +/- 1.900% and 3.440 +/- 1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of concept implementation is presented as an annex to this paper.

Abstract
In lung cancer diagnosis, the design of robust Computer Aided Diagnosis (CAD) systems needs to include an adequate differentiation of benign from malignant nodules. This paper presents a CAD system for the classification of lung nodules in chest Computed Tomography (CT) scans as the way to diagnose lung cancer. The proposed method measures a set of 295 heterogeneous characteristics, including morphology, intensity or texture features, that were used as input of different KNN and SVM classifiers. The system was modeled and trained using a groundtruth provided by specialists taken from a public lung image dataset, the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). This image dataset includes chest CT scans with lung nodule location together with information about the degree of malignancy, among other properties, provided by multiple expert clinicians. In particular, the computed degree of malignancy try to follow the manual labeling by the different radiologists. Promising results were obtained with a first order SVM with an exponential kernel achieving an area under the receiver operating characteristic curve of 96.2 +/- 0.5% when compared with the groundtruth provided in the public CT lung image dataset.

Abstract
Microcalcifications are early indicators of breast cancer that appear on mammograms as small bright regions within the breast tissue. To assist screening radiologists in reading mammograms, supervised learning techniques have been found successful to detect micro-calcifications automatically. Among them, Convolutional Neural Networks (CNNs) can automatically learn and extract low-level features that capture contrast and spatial information, and use these features to build robust classifiers. Therefore, spatial enhancement that enhances local contrast based on spatial context is expected to positively influence the learning task of the CNN and, as a result, its classification performance. In this work, we propose a novel spatial enhancement technique for microcalcifications based on the removal of haze, an apparently unrelated phenomenon that causes image degradation due to atmospheric absorption and scattering. We tested the influence of dehazing of digital mammograms on the microcalcification detection performance of two CNNs inspired by the popular AlexNet and VGGnet. Experiments were performed on 1, 066 mammograms acquired with GE Senographe systems. Statistically significantly better microcalcification detection performance was obtained when dehazing was used as preprocessing. Results of dehazing were superior also to those obtained with Contrast Limited Adaptive Histogram Equalization (CLAHE).

Abstract
The optical dispersion and water content of human liver were experimentally studied to estimate the optical dispersions of tissue scatterers and dry matter. Using temporal measurements of collimated transmittance [T-c(t)] of liver samples under treatment at different glycerol concentrations, free water and diffusion coefficient (D-gl) of glycerol in liver were found as 60.0% and 8.2 x 10(-7) cm(2)/s, respectively. Bound water was calculated as the difference between the reported total water of 74.5% and found free water. The optical dispersion of liver was calculated from the measurements of refractive index (Rl) of tissue samples made for different wavelengths between 400 and 1000 nm. Using liver and water optical dispersions at 20 degrees C and the free and total water, the dispersions for liver scatterers and dry matter were calculated. The estimated dispersions present a decreasing behavior with wavelength. The dry matter dispersion shows higher Rl values than liver scatterers, as expected. Considering 600 nm, dry matter has an Rl of 1.508, whereas scatterers have an Rl of 1.444. These dispersions are useful to characterize the Rl matching mechanism in optical clearing treatments, provided that [T-c(t)] and thickness measurements are performed during treatment. The knowledge of D-gl is also important for living tissue cryoprotection applications. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)