Bioimage Informatics draws upon advances in signal processing, optics, probe chemistry, molecular biology and machine learning to provide answers to biological questions from the growing numbers of biological images acquired in digital form. Microscopy is one of the oldest biological methods, and for centuries it has been paired with visual interpretation to learn about biological phenomena. With the advent of sensitive digital cameras and the dramatic increase in computer processing speeds over the past two decades, it has become increasingly common to collect large volumes of biological image data that create a need for sophisticated image processing and analysis. In addition, dramatic advances in machine learning during the same period set the stage for converting imaging from an observational to a computational discipline and allow the direct generation of biological knowledge from images.
Specialization Electives (3 credits/9 units)
Automation of Biological Research
This course covers principles and applications of optical methods in the study of structure and function in biological systems. Topics to be covered include: absorption and fluorescence spectroscopy; interaction of light with biological molecules, cells, and systems; design of fluorescent probes and optical biosensor molecules; genetically expressible optical probes; photochemistry; optics and image formation; transmitted-light and fluorescence microscope systems; laser-based systems; scanning microscopes; electronic detectors and cameras: image processing; multi-mode imaging systems; microscopy of living cells; and the optical detection of membrane potential, molecular assembly, transcription, enzyme activity, and the action of molecular motors. This course is particularly aimed at students in science and engineering interested in gaining in-depth knowledge of modern light microscopy.
This course introduces the fundamental techniques used in computer vision, that is, the analysis of patterns in visual images to reconstruct and understand the objects and scenes that generated them. Topics covered include image formation and representation, camera geometry and calibration, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, image segmentation, object recognition. The material is based on graduate-level texts augmented with research papers, as appropriate. Evaluation is based on homeworks and final project. The homeworks involve considerable Matlab programming exercises.
Medical Image Analysis
The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the National Library of Medicine Insight Toolkit (ITK), a new software library developed by a consortium of institutions including CMU. In addition to image analysis, the course will describe the major medical imaging modalities and include interaction with practicing radiologists at UPMC.
Special Topics: Registration Problems in Bioimaging
This course will cover the fundamentals of image matching (registration) methods with applications to biomedical engineering. As the fundamental step in image data fusion, registration methods have found wide ranging applications in biomedical engineering, as well as other engineering areas, and have become a major topic in image processing research. Specific topics to be covered include manual and automatic landmark-based, intensity-based, rigid, and nonrigid registration methods. Applications to be covered include multi-modal image data fusion, artifact (motion and distortion) correction and estimation, atlas-based segmentation, and computational anatomy. Course work will include Matlab programming exercises, reading of scientific papers, and independent projects. Upon successful completion, the student will be able to develop his/ her own solution to an image processing problem that involves registration. Prerequisites: 18-396 Signals and Systems or permission of the instructor, working knowledge of Matlab, and some image processing experience.
This course consists of six state-of-the-art imaging techniques (i.e., MRI, MRS, fMRI, PET, MEG/EEG and Optical). Each part of the module will present indepth analysis of the each technique and its application in neuroscience research. Apart from in-depth presentation of the each technique, students will also get acquainted with the operation of the respective instruments. Tour to that respective facility will be guided by the concerned faculty and scientific staff member in that respective facility will assist for demonstration. This course is a joint program between PITT and CMU.