In this thesis, we present an algorithm for the automated detection of abnormalities (targets) in ultrasound images. The algorithm uses little a priori information and does not require training data. The proposed scheme is a combination of the CLEAN algorithm, originally proposed for radio astronomy, and constant false alarm rate (CFAR) processing, developed for use in radar systems. Neither of these algorithms appears to have been previously used for target detection in ultrasound images. The CLEAN algorithm identifies areas in the ultrasound image that stand out above a threshold in relation to the background; CFAR techniques allow for an automated and adaptive selection of the threshold. The algorithm was tested on simulated B-mode images. Using a contrast-detail analysis, probability of detection curves indicate that, depending on the contrast, the method has considerable promise for the automated detection of abnormalities with diameters greater than a few millimetres.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/31333 |
| Date | 14 December 2011 |
| Creators | Masoom, Hassan |
| Contributors | Adve, Raviraj, Cobbold, Richard |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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