Graph Algorithmic Techniques for Biomedical Image Segmentation: LOGISMOS

Speaker:        Prof. Milan Sonka
                The Iowa Institute for Biomedical Imaging
                The University of Iowa

Title:          "Graph Algorithmic Techniques for Biomedical Image
                 Segmentation: LOGISMOS"

Date:           Tuesday, 3 November 2015

Time:           4:30pm to 5:30pm

Venue:          Lecture Theater E, Chia-Wei Woo Academic Concourse, HKUST

Abstract:

Accurate and reliable image segmentation is of paramount importance in
medical image analysis. With a widespread use of 3D/4D imaging modalities
like MR, MDCT, ultrasound, or OCT in routine clinical practice, physicians
are faced with ever-increasing amounts of image data to analyze and
quantitative outcomes of such analyses are increasingly important. Yet,
daily interpretation of clinical images is still typically performed
visually and qualitatively, with quantitative analysis being an exception
rather than the norm. Since performing organ/object segmentations in 3D or
4D is infeasible for a human observer in clinical setting due to the time
constraints, quantitative and highly automated analysis methods must be
developed. Situation is similar when analyzing research animal images or
biological images of living cells from microscopic images. Utilizing
contextual information of mutually related surfaces and objects is
hypothesized to increase segmentation robustness.

Our method for simultaneous segmentation of multiple interacting surfaces
belonging to multiple interacting objects will be presented. The reported
method is part of the family of graph-based image segmentation methods
dubbed LOGISMOS for Layered Optimal Graph Image Segmentation of multiple
Objects and Surfaces. This family of methods guarantees solution
optimality with directly applicability to n-D problems. LOGISMOS is
generally applicable to a multitude of image segmentation problems and its
utility and performance will be demonstrated on cardiovascular MR,
pulmonary CT, orthopaedic MR,  and ophthalmic OCT images showing the broad
applicability of the developed algorithmic concepts.

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Biography:

Professor Milan Sonka is Lowell C. Battershell Chair in Biomedical
Engineering, The University of Iowa, USA. He is currently the Associate
Dean for Graduate Programs and Research, College of Engineering, The
University of Iowa. He is the Professor of Electrical and Computer
Engineering, Applied Mathematical and Computational Sciences,
Ophthalmology and Visual Sciences, and Radiation Oncology. His research
areas are Image Processing and Analysis, and Medical Image Analysis.

http://www.engineering.uiowa.edu/ece/faculty-staff/milan-sonka
[www.engineering.uiowa.edu]