Human Visual System-Based Multi-Scale Tools with Biomedical and Security Applications.
Nercessian, Shahan.
2012
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Abstract: Multi-scale
transforms have been shown to be invaluable tools for image processing. The
effectiveness of consequently formulated multi-scale algorithms have practically made
them de facto standards for realizing solutions for a broad range of image processing
problems. Multi-scale formulations of transforms and algorithms are motivated by the
ability of the human visual system (HVS) ... read moreto extract edge structures at their different
scales. Image processing algorithms, consequently, have been developed which alter
multi-transform coefficients of images for various means. However, the multi-scale
contrasts as defined by these schemes generally not consistent with many other relevant
HVS phenomena. Upon reviewing relevant HVS characteristics, new tools which are
consistent with these features are presented. Accordingly, new image enhancement, image
de-noising, and image fusion algorithms which make use of HVS-inspired multi-scale tools
are presented as contributions to each of these fields. In this context, the aim of the
presented algorithms is two-fold: The intention is to both consider new multi-scale
solutions, as well as to formulate them using perceptually-driven mathematical
constructs based on HVS characteristics. In the context of image enhancement, a new set
of multi-scale image enhancement algorithms are presented which are able to
simultaneously provide both local and global enhancements within a direct enhancement
framework. For the purpose of image de-noising, a multi-scale formulation of the
non-local-means de-noising algorithm is developed which is shown to both visually and
quantitatively outperform existing de-noising approaches. Many algorithms to achieve
image fusion based on the presented transforms are presented. One set of algorithms is
based on a Parameterized Logarithmic Image Processing model, while another is based on
an adaptive similarity-based weighting scheme. The interdependence between the different
algorithms considered in this dissertation is also examined. A joint fusion and
de-noising framework to simultaneous fuse and de-noise images is presented, as well as a
patent pending system using fusion methodologies to perform various tasks. Experimental
results illustrate the effectiveness of the proposed methods by both qualitative and
quantitative means. The benefits of the presented methods are also validated through
practical task-based evaluations in biomedical and security applications, including the
automatic detection of masses in mammograms.
Thesis (Ph.D.)--Tufts University, 2012.
Submitted to the Dept. of Electrical Engineering.
Advisor: Karen Panetta.
Committee: Karen Panetta, Sos Agaian, Joseph Noonan, and Michael Levin.
Keyword: Electrical engineering.read less - ID:
- 41687w00h
- Component ID:
- tufts:20947
- To Cite:
- TARC Citation Guide EndNote
