Current Issue : July - September Volume : 2016 Issue Number : 3 Articles : 4 Articles
Background: CADe and CADx systems for the detection and diagnosis of lung cancer\nhave been important areas of research in recent decades. However, these areas are\nbeing worked on separately. CADe systems do not present the radiological characteristics\nof tumors, and CADx systems do not detect nodules and do not have good levels\nof automation. As a result, these systems are not yet widely used in clinical settings.\nMethods: The purpose of this article is to develop a new system for detection and\ndiagnosis of pulmonary nodules on CT images, grouping them into a single system for\nthe identification and characterization of the nodules to improve the level of automation.\nThe article also presents as contributions: the use of Watershed and Histogram\nof oriented Gradients (HOG) techniques for distinguishing the possible nodules from\nother structures and feature extraction for pulmonary nodules, respectively. For the\ndiagnosis, it is based on the likelihood of malignancy allowing more aid in the decision\nmaking by the radiologists. A rule-based classifier and Support Vector Machine (SVM)\nhave been used to eliminate false positives.\nResults: The database used in this research consisted of 420 cases obtained randomly\nfrom LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and\nthe detection system showed a sensitivity of 94.4 % with 7.04 false positives per case.\nDifferent types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with\ndiameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy\nour system presented ROC curves with areas of: 0.91 for nodules highly unlikely\nof being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for\nnodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of\nbeing malignant and 0.83 for nodules highly suspicious of being malignant.\nConclusions: From our preliminary results, we believe that our system is promising\nfor clinical applications assisting radiologists in the detection and diagnosis of lung\ncancer....
Background: The cardiac parameters, such as heart rate (HR) and heart rate variability\n(HRV), are very important physiological data for daily healthcare. Recently, the camera based\nphoto plethysmography techniques have been proposed for HR measurement.\nThese techniques allow us to estimate the HR contactlessly with low-cost camera.\nHowever, the previous works showed limit success for estimating HRV because the Rââ?¬â??R\nintervals, the primary data for HRV calculation, are sensitive to noise and artifacts.\nMethods: This paper proposed a non-contact method to extract the blood volume\npulse signal using a chrominance-based method followed by a proposed CWT-based\ndenoising technique. The Rââ?¬â??R intervals can then be obtained by finding the peaks in\nthe denoised signal. In this paper, we taped 12 video clips using the frontal camera of\na smart phone with different scenarios to make comparisons among our method and\nthe other alternatives using the absolute errors between the estimated HRV metrics\nand the ones obtained by an ECG-accurate chest band.\nResults: As shown in experiments, our algorithm can greatly reduce absolute errors\nof HRV metrics comparing with the related works using RGB color signals. The mean of\nabsolute errors of HRV metrics from our method is only 3.53 ms for the static-subject\nvideo clips.\nConclusions: The proposed camera-based method is able to produce reliable HRV\nmetrics which are close to the ones measured by contact devices under different conditions.\nThus, our method can be used for remote health monitoring in a convenient\nand comfortable way....
Background: We have developed an improved pediatric vision screener (PVS) that\ncan reliably detect central fixation, eye alignment and focus. The instrument identifies\nrisk factors for amblyopia, namely eye misalignment and defocus.\nMethods: The device uses the birefringence of the human fovea (the most sensitive\npart of the retina). The optics have been reported in more detail previously. The present\narticle focuses on the electronics and the analysis algorithms used. The objective of this\nstudy was to optimize the analog design, data acquisition, noise suppression techniques,\nthe classification algorithms and the decision making thresholds, as well as to\nvalidate the performance of the research instrument on an initial group of young test\nsubjectsââ?¬â?18 patients with known vision abnormalities (eight male and 10 female),\nages 4ââ?¬â??25 (only one above 18) and 19 controls with proven lack of vision issues. Four\nstatistical methods were used to derive decision making thresholds that would best\nseparate patients with abnormalities from controls. Sensitivity and specificity were\ncalculated for each method, and the most suitable one was selected.\nResults: Both the central fixation and the focus detection criteria worked robustly and\nallowed reliable separation between normal test subjects and symptomatic subjects.\nThe sensitivity of the instrument was 100 % for both central fixation and focus detection.\nThe specificity was 100 % for central fixation and 89.5 % for focus detection. The\noverall sensitivity was 100 % and the overall specificity was 94.7 %.\nConclusions: Despite the relatively small initial sample size, we believe that the PVS\ninstrument design, the analysis methods employed, and the device as a whole, will\nprove valuable for mass screening of children....
Background: Areas with high frequency activity within the atrium are thought to be\nââ?¬Ë?driversââ?¬â?¢ of the rhythm in patients with atrial fibrillation (AF) and ablation of these areas\nseems to be an effective therapy in eliminating DF gradient and restoring sinus rhythm.\nClinical groups have applied the traditional FFT-based approach to generate the threedimensional\ndominant frequency (3D DF) maps during electrophysiology (EP) procedures\nbut literature is restricted on using alternative spectral estimation techniques\nthat can have a better frequency resolution that FFT-based spectral estimation.\nMethods: Autoregressive (AR) model-based spectral estimation techniques, with\nemphasis on selection of appropriate sampling rate and AR model order, were implemented\nto generate high-density 3D DF maps of atrial electrograms (AEGs) in persistent\natrial fibrillation (persAF). For each patient, 2048 simultaneous AEGs were recorded\nfor 20.478 s-long segments in the left atrium (LA) and exported for analysis, together\nwith their anatomical locations. After the DFs were identified using AR-based spectral\nestimation, they were colour coded to produce sequential 3D DF maps. These maps\nwere systematically compared with maps found using the Fourier-based approach.\nResults: 3D DF maps can be obtained using AR-based spectral estimation after AEGs\ndownsampling (DS) and the resulting maps are very similar to those obtained using\nFFT-based spectral estimation (mean 90.23 %). There were no significant differences\nbetween AR techniques (p = 0.62). The processing time for AR-based approach was\nconsiderably shorter (from 5.44 to 5.05 s) when lower sampling frequencies and model\norder values were used. Higher levels of DS presented higher rates of DF agreement\n(sampling frequency of 37.5 Hz).\nConclusion: We have demonstrated the feasibility of using AR spectral estimation\nmethods for producing 3D DF maps and characterised their differences to the maps\nproduced using the FFT technique, offering an alternative approach for 3D DF computation\nin human persAF studies....
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