Current Issue : July - September Volume : 2015 Issue Number : 3 Articles : 5 Articles
Background: The pupillary light reflex characterizes the direct and consensual response\nof the eye to the perceived brightness of a stimulus. It has been used as indicator of\nboth neurological and optic nerve pathologies. As with other eye reflexes, this reflex\nconstitutes an almost instantaneous movement and is linked to activation of the same\nmidbrain area. The latency of the pupillary light reflex is around 200 ms, although the\nliterature also indicates that the fastest eye reflexes last 20 ms. Therefore, a system with\nsufficiently high spatial and temporal resolutions is required for accurate assessment. In\nthis study, we analyzed the pupillary light reflex to determine whether any small\ndiscrepancy exists between the direct and consensual responses, and to ascertain\nwhether any other eye reflex occurs before the pupillary light reflex.\nMethods: We constructed a binocular video-oculography system two high-speed\ncameras that simultaneously focused on both eyes. This was then employed to assess\nthe direct and consensual responses of each eye using our own algorithm based on\nCircular Hough Transform to detect and track the pupil. Time parameters describing\nthe pupillary light reflex were obtained from the radius time-variation. Eight healthy\nsubjects (4 women, 4 men, aged 24ââ?¬â??45) participated in this experiment.\nResults: Our system, which has a resolution of 15 microns and 4 ms, obtained time\nparameters describing the pupillary light reflex that were similar to those reported\nin previous studies, with no significant differences between direct and consensual\nreflexes. Moreover, it revealed an incomplete reflex blink and an upward eye\nmovement at around 100 ms that may correspond to Bellââ?¬â?¢s phenomenon.\nConclusions: Direct and consensual pupillary responses do not any significant\ntemporal differences. The system and method described here could prove useful\nfor further assessment of pupillary and blink reflexes. The resolution obtained revealed\nthe existence reported here of an early incomplete blink and an upward eye movement....
Background: It has been reported that one of the main mechanisms that induces\nthe activation of the cochlea through infrared laser light is the photothermal effect.\nThe temperature in the spiral ganglion cells increases as a result of photon absorption.\nHowever, heat conduction can induce an increase in the temperature within the\ncochlea and change the spatial selectivity of activation.\nMethods: We analyzed the effects of heat conduction on the increase in temperature\nwithin the cochlea using a 3D model that simplifies the spiraled cochlea as a rotational\nsymmetric structure . The model is solved using the finite element method.\nResults: Taken as an example, the cochlea is stimulated by laser pulses at eight sites in\nits first turn. The temperature rise in time domain and spatial domain is simulated\nfor different laser pulse energies and repetition rates. The results demonstrate that\nthe temperature in the cochlea increases as the laser pulse energy and repetition\nrate increase. Additionally, the zone affected by the laser is enlarged because of the\nheat conduction in the surrounding structures. As a result, more auditory neurons\ncan be stimulated than the expected.\nConclusions: The heat conduction affects the laser spatial selectivity however, by\nadjusting the stimulation schemes of the laser pulse-trains, such as laser repetition\nrate and laser power, the laser selectivity can be optimized....
Background: Colour image segmentation is fundamental and critical for quantitative\nhistological image analysis. The complexity of the microstructure and the approach\nto make histological images results in variable staining and illumination variations.\nAnd ultra-high resolution of histological images makes it is hard for image segmentation\nmethods to achieve high-quality segmentation results and low computation cost at the\nsame time.\nMethods: Mean Shift clustering approach is employed for histological image\nsegmentation. Colour histological image is transformed from RGB to CIE L*a*b*\ncolour space, and then a* and b* components are extracted as features. To speed up\nMean Shift algorithm, the probability density distribution is estimated in feature space\nin advance and then the Mean Shift scheme is used to separate the feature space into\ndifferent regions by finding the density peaks quickly. And an integral scheme is\nemployed to reduce the computation cost of mean shift vector significantly. Finally\nimage pixels are classified into clusters according to which region their features fall\ninto in feature space.\nResults: Numerical experiments are carried on liver fibrosis histological images.\nExperimental results demonstrate that Mean Shift clustering achieves more\naccurate results than k-means but is computational expensive, and the speed of\nthe improved Mean Shift method is comparable to that of k-means while the\naccuracy of segmentation results is the same as that achieved using standard Mean\nShift method.\nConclusions: An effective and reliable histological image segmentation approach is\nproposed in this paper. It employs improved Mean Shift clustering, which is speed up\nby using probability density distribution estimation and the integral scheme...
Background: Classification of breast ultrasound (BUS) images is an important step in\nthe computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel\nphase-based texture descriptor is proposed for efficient and robust classifiers to\ndiscriminate benign and malignant tumors in BUS images.\nMethod: The proposed descriptor, namely the phased congruency-based binary\npattern (PCBP) is an oriented local texture descriptor that combines the phase\ncongruency (PC) approach with the local binary pattern (LBP). The support vector\nmachine (SVM) is further applied for the tumor classification. To verify the efficiency\nof the proposed PCBP texture descriptor, we compare the PCBP with other three\nstate-of-art texture descriptors, and experiments are carried out on a BUS image\ndatabase including 138 cases. The receiver operating characteristic (ROC) analysis\nis firstly performed and seven criteria are utilized to evaluate the classification\nperformance using different texture descriptors. Then, in order to verify the\nrobustness of the PCBP against illumination variations, we train the SVM\nclassifier on texture features obtained from the original BUS images, and\nuse this classifier to deal with the texture features extracted from BUS images\nwith different illumination conditions (i.e., contrast-improved, gamma-corrected\nand histogram-equalized). The area under ROC curve (AUC) index is used as the\nfigure of merit to evaluate the classification performances.\nResults and conclusions: The proposed PCBP texture descriptor achieves the\nhighest values (i.e. 0.894) and the least variations in respect of the AUC index,\nregardless of the gray-scale variations. It�s revealed in the experimental results\nthat classifications of BUS images with the proposed PCBP texture descriptor are\nefficient and robust, which may be potentially useful for breast ultrasound CADs....
Background: Myoelectric controlled prosthetic hand requires machine based\nidentification of hand gestures using surface electromyogram (sEMG) recorded from\nthe forearm muscles. This study has observed that a sub-set of the hand gestures\nhave to be selected for an accurate automated hand gesture recognition, and reports a\nmethod to select these gestures to maximize the sensitivity and specificity.\nMethods: Experiments were conducted where sEMG was recorded from the muscles\nof the forearm while subjects performed hand gestures and then was classified off-line.\nThe performances of ten gestures were ranked using the proposed Positiveââ?¬â??Negative\nPerformance Measurement Index (PNM), generated by a series of confusion matrices.\nResults: When using all the ten gestures, the sensitivity and specificity was 80.0% and\n97.8%. After ranking the gestures using the PNM, six gestures were selected and these\ngave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand\nclose, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion.\nConclusion: This work has shown that reliable myoelectric based human computer\ninterface systems require careful selection of the gestures that have to be recognized\nand without such selection, the reliability is poor...
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