A Genetically Optimized Level Set Approach to Segmentation of Thyroid Ultrasound Images

Abstract. This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach named Variable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules

Multichannel Raman Gas Analyzer: The Data Acquisition and Control System. Measurement Improvement With Blue Laser Light

Abstract—In this paper, the data acquisition and control system of a multichannel Raman effect-based gas analysis device is presented, together with the improvements achieved in measurement of gas concentration sensitivities as a result of the operation of the system with a new blue laser-light source. The multichannel Raman gas sensor (MRGS) is based on the linear Raman scattering effect and uses photo multiplier tubes (PMTs) in the photon-counting mode of operation. An embedded microcontroller-based data acquisition and control (MDAC) system collects, digitizes, processes, and stores in real time the data from six photon-counting modules and the accompanying sensors, along with an overall system control through appropriate actuators. Furthermore, the MDAC system supports the remote operation of the MRGS device from a host computer through a serial link for the modification of the operating parameters, the downloading of new software versions, and the uploading of the collected data for further analysis. The overall device is controlled by the MDAC system and can operate daily, either manually or in a completely automated mode. Many software and hardware features of the MDAC system described in this article can be adapted to other microcontroller-based acquisition and control systems. Recent advances in the development of solid-state laser sources have enabled the use of a new, state-of-the-art, blue laser for the excitation of the Raman effect. Using this blue laser source, improvements in the sensitivities in measurements of concentration for all tested gases (SO2, CO2, CO, NO2, C6H6, and N2) have been substantiated, compared with the green laser source previously used and reported in a related article. Index Terms—Air-pollution, data acquisition, gas analysis, microcontroller, photon counting, Raman spectroscopy, real-time processing. I

Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images

Abstract—This paper presents a computer-aided approach for nodule delineation in thyroid ultrasound (US) images. The developed algorithm is based on a novel active contour model, named variable background active contour (VBAC), and incorporates the advantages of the level set region-based active contour without edges (ACWE) model, offering noise robustness and the ability to delineate multiple nodules. Unlike the classic active contour models that are sensitive in the presence of intensity inhomogeneities, the proposed VBAC model considers information of variable background regions. VBAC has been evaluated on synthetic images, as well as on real thyroid US images. From the quantification of the results, two major impacts have been derived: 1) higher average accuracy in the delineation of hypoechoic thyroid nodules, which exceeds 91%; and 2) faster convergence when compared with the ACWE model. Index Terms—Active contour models, computer-aided diagnosis, level sets, thyroid nodules, ultrasound (US). I

Computer-Aided Tumor Detection in Endoscopic Video Using Color Wavelet Features

We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity