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Small Blob Detection in Medical Images

Abstract Recent advances in medical imaging technology have greatly enhanced imaging based diagnosis which requires computational effective and accurate algorithms to process the images (e.g., measure the objects) for quantitative assessment. In this dissertation, one type of imaging objects is of interest: small blobs. Example small blob objects are cells in histopathology images, small breast lesions in ultrasound images, glomeruli in kidney MR images etc. This problem is particularly challenging because the small blobs often have inhomogeneous intensity distribution and indistinct boundary against the background.

This research develops a generalized four-phased system for small blob detections. The system includes (1) raw image transformation, (... (more)
Created Date 2015
Contributor Zhang, Min (Author) / Wu, Teresa (Advisor) / Li, Jing (Committee member) / Pavlicek, William (Committee member) / Askin, Ronald (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering / Information science / Medical imaging and radiology / blob detection / feature extraction / Gaussian mixture models / Hessian analysis / image analysis / variational bayesian
Type Doctoral Dissertation
Extent 144 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Industrial Engineering 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis