Current Issue : April - June Volume : 2015 Issue Number : 2 Articles : 5 Articles
Background: Intensity in homogeneity occurs in many medical images, especially in\nvessel images. Overcoming the difficulty due to image in homogeneity is crucial for the\nsegmentation of vessel image.\nMethods: This paper proposes a localized hybrid level-set method for the\nsegmentation of 3D vessel image. The proposed method integrates both local region\ninformation and boundary information for vessel segmentation, which is essential for\nthe accurate extraction of tiny vessel structures. The local intensity information is firstly\nembedded into a region-based contour model, and then incorporated into the\nlevel-set formulation of the geodesic active contour model. Compared with the preset\nglobal threshold based method, the use of automatically calculated local thresholds\nenables the extraction of the local image information, which is essential for the\nsegmentation of vessel images.\nResults: Experiments carried out on the segmentation of 3D vessel images\ndemonstrate the strengths of using locally specified dynamic thresholds in our level-set\nmethod. Furthermore, both qualitative comparison and quantitative validations have\nbeen performed to evaluate the effectiveness of our proposed model.\nConclusions: Experimental results and validations demonstrate that our proposed\nmodel can achieve more promising segmentation results than the original hybrid\nmethod does....
Recently, due to rapidly increase in the diabetic patients, diabetes is one of the biggest health challenges in the age group of 22-79 years. Due to the diabetes, patient suffers cataracts, glaucoma and blood vessels inside the eye may damage sight of the patient. This condition is known as “diabetic retinopathy”. Diabetic retinopathy may cause blindness. As Ophthalmology is a significant branch of biomedical field so there is significant need for the computer-aided automated techniques for the pathology identification in human eyes. These automated techniques must be highly accurate and time efficient. In this paper, we primarily review on the importance of identification of all the retinal diseases caused by the diabetes. We also review and analyze the automated techniques, optimized algorithms and methodologies used for the identification of the early and advance stages of the retinal diseases caused by diabetes along with the severity of the disease. The proposed work also gives information of available public data set on which algorithms are tested and trained. This paper also briefly touches the imaging method involved for acquiring the fundus, the gold standard rule for data set and imaging....
Background: Frequency selectivity (FS) is an important aspect of auditory function,\nand is typically described by a tuning curve function. Sharply tuned curves represent\na higher acuity in detecting frequency differences, and conversely, broadly tuned\ncurves demonstrate a lower acuity. One way of obtaining tuning curves is from\ntechniques based on subjective behavioral responses, which yields psychophysical\ntuning curves (PTCs). In contrast, other methods rely on objective auditory responses\nto sound, such as neuron responses and otoacoustic emissions, amongst others. The\npresent study introduces an objective method that uses stimulus frequency\notoacoustic emissions (SFOAEs) to assemble suppression tuning curves (STCs).\nFinding an objective method of accurately measuring human FS is very important, as\nit would permit the FS to be assayed in non-responsive patients (e.g., neonates or\ncomatose patients). However, before the objective method can be applied, it must\nbe demonstrated that its ability to estimate the FS, gives comparable results to those\nobtained by subjective procedures i.e. PTCs.\nMethods: SFOAEs responses, generated in the peripheral auditory system, were used\nto produce STCs. PTCs were measured by behavioral responses. The validity of the\nobjective measures of human FS were determined by comparing stimulus frequency\notoacoustic emission suppression tuning curves (SFOAE STCs) to PTCs at common\nstimulus parameters in 10 individuals with normal hearing, at low probe-tone levels.\nResults: The average Q10 ratios measured between PTCs and SFOAE STCs from\nsubjects were close to 1 at various center frequencies (F 2,24 = .15, p = .858). The\nestimates of FS provided by SFOAE STCs and PTCs were similar.\nConclusions: This system could be used to estimate auditory FS by both objective\nand subjective methods. SFOAE STCs have the potential to provide an objective\nestimate of auditory FS.\nKeywords: Stimulus frequ...
There is an established tradition of cardiovascular simulation tools, but the\napplication of this kind of technology in the e-Learning arena is a novel approach.\nThis paper presents an e-Learning environment aimed at teaching the interaction of\ncardiovascular and lung systems to health-care professionals. Heart-lung interaction\nmust be analyzed while assisting patients with severe respiratory problems or with\nheart failure in intensive care unit. Such patients can be assisted by mechanical\nventilatory assistance or by thoracic artificial lung.\nââ?¬Å?In silicoââ?¬Â cardiovascular simulator was experimented during a training course given\nto graduate students of the School of Specialization in Cardiology at ââ?¬Ë?Sapienzaââ?¬â?¢\nUniversity in Rome.\nThe training course employed CARDIOSIMÃ?©: a numerical simulator of the cardiovascular\nsystem. Such simulator is able to reproduce pathophysiological conditions of patients\naffected by cardiovascular and/or lung disease. In order to study the interactions\namong the cardiovascular system, the natural lung and the thoracic artificial lung (TAL),\nthe numerical model of this device has been implemented. After having reproduced a\npatientââ?¬â?¢s pathological condition, TAL model was applied in parallel and hybrid model\nduring the training course.\nResults obtained during the training course show that TAL parallel assistance reduces\nright ventricular end systolic (diastolic) volume, but increases left ventricular end systolic\n(diastolic) volume. The percentage changes induced by hybrid TAL assistance on\nhaemodynamic variables are lower than those produced by parallel assistance. Only in\nthe case of the mean pulmonary arterial pressure, there is a percentage reduction\nwhich, in case of hybrid assistance, is greater (about 40%) than in case of parallel\nassistance (20-30%).\nAt the end of the course, a short questionnaire was submitted to students in order to\nassess the quality of the course. The feedback obtained was positive, showing good\nresults with respect to the degree of studentsââ?¬â?¢ learning and the ease of use of the\nsoftware simulator....
Background: Many researchers have attempted to acquire respiratory rate (RR)\ninformation from a photoplet hysmogram (PPG) because respiration affects the\nwaveform of the PPG. However, most of these methods were difficult to operate in\nreal-time because of their complexity or computational requirements. From these\nneeds, we attempted to develop a method to estimate RR from a PPG with a light\ncomputational burden.\nMethods: To obtain RR information, we adopt a sequential filtering structure and\nfrequency estimation technique, which extracts a dominant frequency from a given\nsignal. In particular, we used an adaptive lattice notch filter (ALNF) to estimate RR\nfrom a PPG along with an additional heart rate that is utilized as an adaptation\nparameter of our method. Furthermore, we designed a sequential infinite impulse\nresponse (IIR) notch filtering system (i.e., harmonic IIR notch filter) to eliminate the\ncardiac component and its harmonics from the PPG. We compared the proposed\nmethod with Burg�s AR modeling method, which is widely used to estimate RR from\na PPG, using open-source data and measured data.\nResults: By using a statistical test, it was determined that our adaptive lattice-type\nrespiratory rate estimator (ALRE) was significantly more accurate than Burg�s AR\nmodel method (p <0.0001). Furthermore, the ALRE�s tracking performance was better\nthan that of Burg�s method, and the variances of its estimates were smaller than\nthose of Burg�s method.\nConclusions: In short, our method showed a better performance than Burg�s AR\nmodeling method for real-time applications....
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