Why finance professors should be teaching Nietzsche

<p><strong>Abstract:</strong> Retinal images (RI) are widely used to diagnose a variety of eye conditions and diseases such as myopia and diabetic retinopathy. They are inherently characterised by having nonuniform illumination and low-contrast homogeneous regions which represent a unique set of challenges for retinal image registration (RIR). This paper investigates using the expectation maximization for principal component analysis based mutual information (EMPCA-MI) algorithm in RIR. It combines spatial features with mutual information to efficiently achieve improved registration performance. Experimental results for mono-modal RI datasets verify that EMPCA-MI<br>together with Powell-Brent optimization affords superior robustness in comparison with existing RIR methods, including the geometrical features method.</p> <p><br><strong>Index Terms</strong>— Image registration, principal component analysis, mutual information, expectation-maximization algorithms, retinopathy.</p> <p> </p> <p><strong>Poster presented at</strong>: 38th International Conference on Acoustics, Speech, and Signal Processing<br>(ICASSP), 26th to 31st May 2013, Vancouver, Canada.<br>doi: 10.1109/ICASSP.2013.6637824</p

Astigmatism and Pseudoaccommodation in Pseudophakic Eyes

noAdvanced IOLs with circumferential zones of different power provide pseudoaccommodation. We investigated the potential for power variation with meridian, namely astigmatism, to provide pseudo-accommodation. With appropriate power and axis orientations, acceptable pseudo-accommodation can be achieved

Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography

Purpose. Microaneurysms (MAs) are considered a hallmark of retinal vascular disease, yet what little is known about them is mostly based upon histology, not clinical observation. Here, we use the recently developed adaptive optics scanning light ophthalmoscope (AOSLO) fluorescein angiography (FA) to image human MAs in vivo and to expand on previously described MA morphologic classification schemes. Methods. Patients with vascular retinopathies (diabetic, hypertensive, and branch and central retinal vein occlusion) were imaged with reflectance AOSLO and AOSLO FA. Ninety-three MAs, from 14 eyes, were imaged and classified according to appearance into six morphologic groups: focal bulge, saccular, fusiform, mixed, pedunculated, and irregular. The MA perimeter, area, and feret maximum and minimum were correlated to morphology and retinal pathology. Select MAs were imaged longitudinally in two eyes. Results. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging revealed microscopic features of MAs not appreciated on conventional images. Saccular MAs were most prevalent (47%). No association was found between the type of retinal pathology and MA morphology (P = 0.44). Pedunculated and irregular MAs were among the largest MAs with average areas of 4188 and 4116 μm2, respectively. Focal hypofluorescent regions were noted in 30% of MAs and were more likely to be associated with larger MAs (3086 vs. 1448 μm2, P = 0.0001). Conclusions. Retinal MAs can be classified in vivo into six different morphologic types, according to the geometry of their two-dimensional (2D) en face view. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging of MAs offers the possibility of studying microvascular change on a histologic scale, which may help our understanding of disease progression and treatment response

Microscopic Inner Retinal Hyper-reflective Phenotypes in Retinal and Neurologic Disease

Purpose. We surveyed inner retinal microscopic features in retinal and neurologic disease using a reflectance confocal adaptive optics scanning light ophthalmoscope (AOSLO). Methods. Inner retinal images from 101 subjects affected by one of 38 retinal or neurologic conditions and 11 subjects with no known eye disease were examined for the presence of hyper-reflective features other than vasculature, retinal nerve fiber layer, and foveal pit reflex. The hyper-reflective features in the AOSLO images were grouped based on size, location, and subjective texture. Clinical imaging, including optical coherence tomography (OCT), scanning laser ophthalmoscopy, and fundus photography was analyzed for comparison. Results. Seven categories of hyper-reflective inner retinal structures were identified, namely punctate reflectivity, nummular (disc-shaped) reflectivity, granular membrane, waxy membrane, vessel-associated membrane, microcysts, and striate reflectivity. Punctate and nummular reflectivity also was found commonly in normal volunteers, but the features in the remaining five categories were found only in subjects with retinal or neurologic disease. Some of the features were found to change substantially between follow up imaging months apart. Conclusions. Confocal reflectance AOSLO imaging revealed a diverse spectrum of normal and pathologic hyper-reflective inner and epiretinal features, some of which were previously unreported. Notably, these features were not disease-specific, suggesting that they might correspond to common mechanisms of degeneration or repair in pathologic states. Although prospective studies with larger and better characterized populations, along with imaging of more extensive retinal areas are needed, the hyper-reflective structures reported here could be used as disease biomarkers, provided their specificity is studied further

Noninvasive imaging of the thirteen-lined ground squirrel photoreceptor mosaic.

Ground squirrels are an increasingly important model for studying visual processing, retinal circuitry, and cone photoreceptor function. Here, we demonstrate that the photoreceptor mosaic can be longitudinally imaged noninvasively in the 13-lined ground squirrel (Ictidomys tridecemlineatus) using confocal and nonconfocal split-detection adaptive optics scanning ophthalmoscopy using 790 nm light. Photoreceptor density, spacing, and Voronoi analysis are consistent with that of the human cone mosaic. The high imaging success rate and consistent image quality in this study reinforce the ground squirrel as a practical model to aid drug discovery and testing through longitudinal imaging on the cellular scale

UVSD: Software for Detection of Color Underwater Features

Underwater Video Spot Detector (UVSD) is a software package designed to analyze underwater video for continuous spatial measurements (path traveled, distance to the bottom, roughness of the surface etc.) Laser beams of known geometry are often used in underwater imagery to estimate the distance to the bottom. This estimation is based on the manual detection of laser spots which is labor intensive and time consuming so usually only a few frames can be processed this way. This allows for spatial measurements on single frames (distance to the bottom, size of objects on the sea-bottom), but not for the whole video transect. We propose algorithms and a software package implementing them for the semi-automatic detection of laser spots throughout a video which can significantly increase the effectiveness of spatial measurements. The algorithm for spot detection is based on the Support Vector Machines approach to Artificial Intelligence. The user is only required to specify on certain frames the points he or she thinks are laser dots (to train an SVM model), and then this model is used by the program to detect the laser dots on the rest of the video. As a result the precise (precision is only limited by quality of the video) spatial scale is set up for every frame. This can be used to improve video mosaics of the sea-bottom. The temporal correlation between spot movements changes and their shape provides the information about sediment roughness. Simultaneous spot movements indicate changing distance to the bottom; while uncorrelated changes indicate small local bumps. UVSD can be applied to quickly identify and quantify seafloor habitat patches, help visualize habitats and benthic organisms within large-scale landscapes, and estimate transect length and area surveyed along video transects

\u3cem\u3eIn vivo\u3c/em\u3e Imaging of Human Retinal Microvasculature Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography

The adaptive optics scanning light ophthalmoscope (AOSLO) allows visualization of microscopic structures of the human retina in vivo. In this work, we demonstrate its application in combination with oral and intravenous (IV) fluorescein angiography (FA) to the in vivo visualization of the human retinal microvasculature. Ten healthy subjects ages 20 to 38 years were imaged using oral (7 and/or 20 mg/kg) and/or IV (500 mg) fluorescein. In agreement with current literature, there were no adverse effects among the patients receiving oral fluorescein while one patient receiving IV fluorescein experienced some nausea and heaving. We determined that all retinal capillary beds can be imaged using clinically accepted fluorescein dosages and safe light levels according to the ANSI Z136.1-2000 maximum permissible exposure. As expected, the 20 mg/kg oral dose showed higher image intensity for a longer period of time than did the 7 mg/kg oral and the 500 mg IV doses. The increased resolution of AOSLO FA, compared to conventional FA, offers great opportunity for studying physiological and pathological vascular processes

Processing of image sequences from fundus camera

Cílem mé diplomové práce bylo navrhnout metodu analýzy retinálních sekvencí, která bude hodnotit kvalitu jednotlivých snímků. V teoretické části se také zabývám vlastnostmi retinálních sekvencí a způsobem registrace snímků z fundus kamery. V praktické části je implementována metoda hodnocení kvality snímků, která je otestována na reálných retinálních sekvencích a vyhodnocena její úspěšnost. Práce hodnotí i vliv této metody na registraci retinálních snímků.The aim of my master's thesis was to propose a method of retinal sequence analysis which will evaluate the quality of each frame. In the theoretical part, I will also deal with the properties of retinal sequences and the way of registering the images of the fundus camera. In the practical part the method of evaluating image quality is implemented. This algorithm is tested on real retinal sequences and its success is assessed. This work also evaluates the impact of proposed method on the registration of retinal images.

Modeling Brain Circuitry over a Wide Range of Scales

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Electron Microscopes (EM) can now provide the nanometer resolution that is needed to image synapses, and therefore connections, while Light Microscopes (LM) see at the micrometer resolution required to model the 3D structure of the dendritic network. Since both the topology and the connection strength are integral parts of the brain's wiring diagram, being able to combine these two modalities is critically important. In fact, these microscopes now routinely produce high-resolution imagery in such large quantities that the bottleneck becomes automated processing and interpretation, which is needed for such data to be exploited to its full potential. In this paper, we briefly review the Computer Vision techniques we have developed at EPFL to address this need. They include delineating dendritic arbors from LM imagery, segmenting organelles from EM, and combining the two into a consistent representation