This paper is a revision of a paper presented at the SPIE conference on Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, Feb. 2005, San Diego, California. The paper presented there appears (unrefereed) in SPIE Proceedings Vol. 5746.
Segmentation, or separating an image into distinct objects, is the key to creating 3-D renderings from serial slice images. This is typically a manual process requiring trained persons to tediously outline and isolate the objects in each image. We describe a template-based semiautomatic segmentation method to aid in the segmentation process and 3-D reconstruction of microscopic objects recorded with a confocal laser scanning microscope (CLSM). The simple and robust algorithm is based on the creation of a user-defined object template, followed by automatic segmentation of the object in each of the remaining image slices. The user guides the process by selecting the initial image slice for the object template, and labeling the object of interest. The algorithm is applied to mathematically defined shapes to verify the performance of the software. The algorithm is then applied to biological samples, including neurons in the common housefly. It is the quest to further understand the visual system of the housefly that provides the opportunity to develop this segmentation algorithm. Further application of this algorithm may extend to other registered and aligned serial section datasets with high contrast objects.