UNC SealOld WellStephen M. Pizer

Kenan Professor

Stephen M. PizerContact Information

Department of Computer Science
University of North Carolina at Chapel Hill
CB #3175, Sitterson Hall
Chapel Hill, NC 27599-3175

(919) 590-6085 (Office)
(919) 590-6105 (Fax)

Email: pizer @ cs.unc.edu

Administrative Assistant:
Alden Sharpe
(919) 590-6151 (phone)
ajsharpe@cs.unc.edu (E-mail)


Research Interests

I lead the UNC Medical Image Display & Analysis Group, a collaborative group of about 90 professionals from the departments of Computer Science, Radiology, Radiation Oncology, Surgery, Psychiatry, Ophthalmology, Pathology, Obstetrics and Gynecology, Mathematics, Biostatistics, and Biomedical Engineering, including 15 graduate students from Computer Science, and 19 from other departments. Major goals include fast interactive 3D display of medical images, particularly image-guided surgery, 2D and 3D recognition, segmentation, registration and shape description, modeling and measuring visual perception of 2D images, contrast enhancement, observer studies of display techniques, and development of parallel graphics and image processing hardware. We use specific problems from diagnoses from medical images (e.g., x-rays, CT scans, magnetic resonance images, ultrasound images, micrographs), radiotherapy, and surgery to motivate general solutions in the areas of image display, image analysis, and visual perception.

Image analysis and shape description. We wish to allow the display, definition, or measurement of selected anatomic structures. With applications in both 2D and 3D from radiology, radiation oncology, surgery, and other medical specialties, the combination of geometric analysis and multiresolution approaches is being used to produce hierarchical object-based image descriptions to be used as a basis for both display and image analysis. We study the description of shape within images, using multiresolution approaches applied to objects' medial properties derived directly from gray-scale images. We use these approaches as the basis of clinical image analysis and 3D volume rendering.

Graphics and 3D fusion (with Fuchs, Coggins, Rosenman, Manocha, Brooks, and Whitted). The Graphics and Image group collaborates on interactive 3D display from volume data, including augmented reality displays. Our present research focuses on exploratory 3D planning of radiotherapy and neurosurgery, fast rendering methods, fusion of multimodality medical images, and, in real time, of preoperative and intraoperative images and of optical and medical 3D images of the patient.

Psychophysics and modeling of human visual perception (with Burbeck). We seek to characterize human extraction and representation of image objects and their shape. This study leads to effective computer vision methods and allows matching image analysis and human vision in interactive display systems. The research includes basic psychophysical experiments and modeling.

Adaptive contrast enhancement and image quality evaluation (with Rosenman, and E. Pisano [Radiology]). The selection of displayed intensity levels for each level recorded in a digital image must vary across the image, depending upon context. We have developed leading methods for doing this contrast enhancement, and we have proven their clinical efficacy. We are developing task-based image quality evaluation methods based on visual models and observer experiments. We attempt to optimize contrast enhancement according to these quality measures.

For more information see:




Writing Help in English: Common punctuation and wording errors in American English

Book - Medial Representations: Mathematics, Algorithms and Applications (password protected)

Comp 550: Fall 2013

MIP Grant Summary Statement (password protected)

MICCAI Fellow Award Presentation Video

MICCAI Workshop Papers

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Last updated 26 February 2009