Research in many fields of life and biomedical sciences depends on the microscopic image analysis of biological images. Quantitative analysis of these images is often time-consuming, tedious, and may be prone to subjective bias from the observer and inter /intra observer variations. Systems for automatic analysis developed in the past decade determine various parameters associated with biological tissue, such as the number of cells, object volume and length of fibers to avoid problems with manual collection of microscopic data. Specifically, automatic analysis of biological microstructures using unbiased stereology, a set of approaches designed to avoid all known sources of systematic error, plays a large and growing role in bioscience research. Our aim is to develop an algorithm that automates and increases the throughput of a commercially available, computerized stereology device (Stereologer, Stereology Resource Center, Chester, MD). The current method for estimation of first and second order parameters of biological microstructures requires a trained user to manually select biological objects of interest (cells, fibers etc.) while systematically stepping through the three dimensional volume of a stained tissue section. The present research proposes a three-part method to automate the above process: detect the objects, connect the objects through a z-stack of images (images at varying focal planes) to form a 3D object and finally count the 3D objects. The first step involves detection of objects through learned thresholding or automatic thresholding. Learned thresholding identifies the objects of interest by training on images to obtain the threshold range for objects of interest. Automatic thresholding is performed on gray level images converted from RGB (red-green-blue) microscopic images to detect the objects of interest. Both learned and automatic thresholding are followed by iterative thresholding to separate objects that are close to each other. The second step, linking objects through a z-stack of images involves labeling the objects of interest using connected component analysis and then connecting these labeled objects across the stack of images to produce a 3D object. Finally, the number of linked objects in a 3D volume is counted using the counting rules of stereology. This automatic approach achieves an overall object detection rate of 74%. Thus, these results support the view that automatic image analysis combined with unbiased sampling as well as assumption and model-free geometric probes, provides accurate and efficient quantification of biological objects.

In this study, it is aimed to improve a set of image processing techniques used in a previously developed method for reconstructing 3D parameters of a secondary passive target using single camera images. This 3D reconstruction method was developed and implemented on a setup consisting of a digital camera, a computer, and a positioning unit. Some automatic target recognition techniques were also included in the method. The passive secondary target used is a circle with two internal spots. In order to achieve a real time target detection, the existing binarization, edge detection, and ellipse detection algorithms are debugged, modified, or replaced to increase the speed, to eliminate the run time errors, and to become compatible for target tracking. The overall speed of 20 Hz is achieved for 640x480 pixel resolution 8 bit grayscale images on a 2.8 GHz computer A novel target tracking method with various tracking strategies is introduced to reduce the search area for target detection and to achieve a detection and reconstruction speed at the maximum frame rate of the hardware. Based on the previously suggested lens distortion model, distortion measurement, distortion parameters determination, and distortion correction methods for both radial and tangential distortions are developed. By the implementation of this distortion correction method, the accuracy of the 3D reconstruction method is enhanced. The overall 3D reconstruction method is implemented in an integrated software and hardware environment as a combination of the methods with the best performance among their alternatives. This autonomous and real time system is able to detect the secondary passive target and reconstruct its 3D configuration parameters at a rate of 25 Hz. Even for extreme conditions, in which it is difficult or impossible to detect the target, no runtime failures are observed.