Information theoretic design of breast sonography

Nguyen, Nghia Q.

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https://hdl.handle.net/2142/14624 Copy

Description

Title

Information theoretic design of breast sonography

Author(s)

Nguyen, Nghia Q.

Issue Date

2010-01-06T16:20:02Z

Director of Research (if dissertation) or Advisor (if thesis)

Insana, Michael F.

Doctoral Committee Chair(s)

Insana, Michael F.

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Breast sonography
• ideal observer
• image quality
• spatial filters

Abstract

First-principle approaches to the design of medical ultrasonic imaging systems for specific visual tasks are being explored. Our study focuses on breast cancer diagnosis and is based on the ideal observer concept for visual discrimination tasks, whereby tasks based on five clinical features are expressed mathematically as likelihood functions. Realistic approximations to the ideal strategy for each task are proposed as an additional beamforming procedure to maximize diagnostic image information content available to readers. Our previous study revealed that the Wiener filter, derived as a stationary approximation of the ideal observer and operating on RF echo data, generally improved discriminability except for one case involving high-contrast lesions. This study explores an adaptive, iterative Wiener filter that includes a lesion segmentation algorithm to improve discriminability for high-contrast lesions. Predicted performance is compared with that measured from trained human observers using psychophysical methods. The iterative Wiener filter was found to match the performance of the Wiener filter for low-contrast lesions and increase the performance for high-contrast tasks. This filter offers greater diagnostic performance for discriminating malignant and benign breast lesions, and it provides a rational basis for further task-specific imaging system design. The thesis also addresses some issues that may be encountered when applying the filter in clinical environments. We found that background variability improves the performance of spatial filters. We also studied the limitation of these filtering methods as key assumptions are violated.

Graduation Semester

2009-12

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http://hdl.handle.net/2142/14624

Copyright and License Information

Copyright 2009 Nghia Q. Nguyen

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