In this webinar we will explore approaches to analyzing a series of noisy, heterogeneous microscopic blood-smear images to quantify parasitic infection. Then we will use techniques from computer vision and machine learning and automatically recognize the type of parasite in the image set.

The presentation will be particularly valuable for anyone interested in using MATLAB to process, visualize, and quantify biomedical imagery.  Rather than focus on extracting information from a few homogeneous images, we will introduce a typical real-world challenge, and discuss approaches to managing and exploring collections of widely heterogeneous images.  We will describe user interfaces that simplify the exploration and algorithm development processes, and demonstrate their utility in identifying and quantifying scientific or clinically relevant insights.

We will then focus on the extraction of features from images, and the use of machine learning algorithms to classify images based on their content.

In this presentation, we will:

• Explore and manage a range of real-world image sets
• Solve challenging image processing problems with user interfaces
• Develop familiarity with simple to advanced image segmentation approaches
• Classify parasitic infections using machine learning techniques

About the Presenter:
Brett Shoelson, Ph.D. completed undergraduate degrees at the University of Florida and Mercer University, and his Master's and Doctorate degrees in Biomedical Engineering at Tulane University. His research career has included quantifying retinal blood flow in diabetes at Harvard Medical School and modeling the mechanics of hearing at the National Institutes of Health. During more than ten years of research, Brett developed a passion for solving problems in MATLAB. He has worked as a Principal Application Engineer for MathWorks since 2005, where he has pursued his interest in using MATLAB for image processing.