PubMed2003
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.