Automated Feature Analysis in Biological Images

Abstract

This thesis is comprised of three projects that I worked on during the period of my Master of Science degree. All three projects use computer vision and image processing techniques to improve microscopy image analysis workflows and develop object detection applications. This work also discusses an image analysis tool developed for imaging and analysis of collagen.

The first project is aimed at improving the current state of image acquisition by autofocusing the slide and removing artifacts from image by flat field correction. This project will serve as a stepping stone for smart microscopes where runtime analysis can be done during acquisition.

The second project was developed in collaboration with the Exploratorium Museum (San Francisco) to detect and highlight zebra fish embryos and zebrafish in a stream of video captured by a microscope as the objective is moved or zoomed by users. The aim of this project was to improve museum visitor participation by highlighting all the zebrafish in current field of view.

The third project is aimed at developing data analysis and data visualization tools which use the fiber data extracted from Second Harmonic Generation (SHG) images by CT-FIRE (Curvelet Transform - Fiber Extraction Algorithm) software. Two broad functionalities developed were: Post Processing Graphical User Interface (GUI) for fiber analysis; and Region of Interest (ROI) manager.