A finite element approach to predicting scleral response to mechanical stress.

The technique of finite element analysis was applied to ocular geometry to predict the effects of mechanical loads, such as those imposed by glaucoma shunt implants, on the stress distribution and organization of the collagen fiber matrix that comprises the sclera. Axisymmetric and 2D shell models of the sclera were constructed to simulate the application of a pressure region on the exterior surface of the sclera. Both models predict redistribution of stress from the center of the pressure region to its outer edge where the magnitude of principal stresses exceeds that of any other location in the models. The models are supported by morphological changes in tissue samples from human and rabbit eyes that have been subjected to stresses similar to those depicted in the models. Analysis of scleral collagen using polarized light shows the tissue is highly organized and responsive: Collagen aligns itself along the principal stresses within the sclera. Furthermore, amount of choroidal hemorrhaging commonly associated with glaucoma shunts correlates with redistribution of mechanical stress, suggesting that pressure imposed by plate rather than reduction of intraocular pressure is responsible for hemorrhage.

The segmentation and visualization of a neuron in the housefly's visual system.

Those studying biological systems are often interested in the morphology of the various microscopic organelles. The three dimensional reconstruction and visualization of objects provide a powerful tool to understand the nature of each object, and its relationship to other objects. Segmentation is the key to 3D analysis and study of objects that have been recorded with a series of sectioned images, such as from a confocal laser scanning microscope (CLSM). Segmentation is the process of completely separating or isolating the individual objects in an image. A seed-based semi-automatic segmentation tool has been developed to aid in the process of 3D visualization of objects recorded with serial sectioned images, including a boundary creation method that maintains the separate identity of contacting objects. This segmentation tool also allows the user to retain background information as a separate object, providing important reference and landmark information for the object of interest. This paper summarizes the main parts of the segmentation algorithm and presents 3D reconstructions of visual neurons of the housefly, Musca domestica. These reconstructions are compared to typical 3D images produced from other widely used software packages, including standard CLSM imaging software and the popular ImageJ supported by National Institute of Health (NIH). Efforts are underway to develop a user-friendly graphical user interface (GUI) for the segmentation algorithm to entice broader used in research settings.

Segmentation and 3D reconstruction of biological cells from serial slice images.

Our understanding of the world around us and the many objects that we encounter is based primarily on three-dimensional information. It is simply part of the environment in which we live and the intuitive nature of our interpretation of our surroundings. In the arena of biomedical imaging, the image information most often collected is in the form of two-dimensional images. In cases where serial slice information is obtained, such as MRI images, it is still difficult for the observer to mentally build and understand the three-dimensional structure of the object. Although most image rendering software packages allow for 3D views of the serial sections, they lack the ability to segment, or isolated different objects in the data set. Typically the task of segmentation is performed by knowledgeable persons who tediously outline or label the object of interest in each image slice containing the object [1,2]. It remains a difficult challenge to train a computer to understand an image and aid in this process of segmentation. This article reports of on-going work in developing a semi-automated segmentation technique. The approach uses a Leica Confocal Laser Scanning Microscope (CLSM) to collect serial slice images, image rendering and manipulating software called IMOD (Boulder Colorado), and Matlab (The Mathworks Inc.) image processing tools for development of the object segmentation routines. The initial objects are simple fluorescent microspheres (Molecular Probes), which are easily imaged and segmented. The second objects are rat enteric neurons, which provide medium complexity in shape and size. Finally, the work will be applied to the biological cells of the household .y, Musca domestica, to further understand how its vision system operates.

Biologically based machine vision: signal analysis of monopolar cells in the visual system of Musca domestica.

Machine vision for navigational purposes is a rapidly growing field. Many abilities such as object recognition and target tracking rely on vision. Autonomous vehicles must be able to navigate in dynamic enviroments and simultaneously locate a target position. Traditional machine vision often fails to react in real time because of large computational requirements whereas the fly achieves complex orientation and navigation with a relatively small and simple brain. Understanding how the fly extracts visual information and how neurons encode and process information could lead us to a new approach for machine vision applications. Photoreceptors in the Musca domestica eye that share the same spatial information converge into a structure called the cartridge. The cartridge consists of the photoreceptor axon terminals and monopolar cells L1, L2, and L4. It is thought that L1 and L2 cells encode edge related information relative to a single cartridge. These cells are thought to be equivalent to vertebrate bipolar cells, producing contrast enhancement and reduction of information sent to L4. Monopolar cell L4 is thought to perform image segmentation on the information input from L1 and L2 and also enhance edge detection. A mesh of interconnected L4's would correlate the output from L1 and L2 cells of adjacent cartridges and provide a parallel network for segmenting an object's edges. The focus of this research is to excite photoreceptors of the common housefly, Musca domestica, with different visual patterns. The electrical response of monopolar cells L1, L2, and L4 will be recorded using intracellular recording techniques. Signal analysis will determine the neurocircuitry to detect and segment images.

Image processing and 3D reconstruction of serial section micrographs from Musca Domestica's biological cells responsible for visual processing.

The ability to visualize and understand three-dimensional objects from two-dimensional cross-section or slice images is difficult, even if the observer has a general concept of the object of interest. The focus of this research is to apply image-processing methods to two-dimensional cross-section electron transmission micrographs of the biological cells of the Musca Domestica's, or household fly's visual system in an effort to better understand the cells responsible for processing visual information. The application of knowledge gained from biological systems is know as biomimetics. The first task will be to construct a useful three-dimensional data set from two-dimensional micrographs provided by the U.S. Air Force Academy in Colorado Springs, Colorado. The data set will be constructed by aligning these images in an edge-to-edge fashion to form a layer. Once each layer is reconstructed, the layers will be stacked and registered to form the third dimension of the data set. This task is complicated by the fact that translation, rotation and scaling mismatches exist in the images. The second task will be to segment and label the biological cells of interest. Computerized segmentation has not yet proved successful, resulting in a manual or "brain-powered" approaches being used at many institutions. By using and modifying current computer image-processing techniques, advances leading to a semi-automated segmentation process may result. Finally, the segmented data must be formatted for use with existing software to render and view the cell(s) of interest. A "marching cubes" surface-rendering algorithm is often implemented in current visualization software, along with routines to view, rotate and scale the resulting surfaces in real time. The result of viewing and manipulating the biological data set will be an increased understanding of the processes of the fly's visual system. Other researchers will use the knowledge gained from the three-dimensional renderings of the cells to further develop an analog vision system based on the fly's compound eye. Much of this research is funded by the Navy Air Warfare Center in an effort to design an analog visual system with real-time target identification and tracking capabilities.