Degree Granting Department
Computer Science and Engineering
Dmitry B. Goldgof, Ph.D.
Lawrence O. Hall, Ph.D.
Peter R. Mouton, Ph.D.
Object Count, Learned Thresholding, Automatic Thresholding, Counting Rules
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.
Scholar Commons Citation
Bonam, Om Pavithra, "Automated Quantification of Biological Microstructures Using Unbiased Stereology" (2011). USF Tampa Graduate Theses and Dissertations.