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Digital Image Processing Projects for ..

2014.

SantosJ., Chaudhari AJ, , Ferrero A.,Yang K., Boone JM, Badawi RD,Non-Rigid registration of serial dedicated breast CT, longitudinaldedicated breast CT and PET/CT images using the diffeomorphic demonsmethod, Physica Medica, In Press.

Instruction Begins: January 26, F

of Mathematics, Applied Math Colloquium: , 2010.

, Geometric methods for brain image registration, ,2010.

, ,2009.

, 2009.

, 2008.
,,LONI, CCB Presents, 2008.

, Biomedical Imaging andAnalysis Seminar Series, 2008.

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University of Southern California, Los Angeles
Dept.

efg's Image Processing: Algorithms

Estimation of the Image Quality in Emission Tomography: Application to Optimization of SPECT System Design - Niccolo Fuin - 2013Registration of Magnetic Resonance and Ultrasound Images for Guiding Prostate Cancer Interventions - Yipeng Hu - 2013 Registration of prone and supine CT colonography images and its clinical application - Holger Roth - 2013 - JM Machado Cardoso - 2012 .

If your thesis incorporates images, photos, maps, diagrams, etc., not created by you, copyright permission must be obtained from the copyright holder of those works to use their content within your thesis. A copy of each permission must be uploaded to the Thesis Registration System.

Links to many different image processing algorithms

It is common practice for museums to digitize their collections, mainly for preservation, documentation and dissemination purposes, but one can go further than simply archival: digital acquisition enables digital analysis of paintings. This proves to be a useful, non-invasive tool, for many applications such as restoration, conservation, art history, material and structure characterization, authentication, dating and even style analysis.

Before being able to do such analysis, images from various modalities need to be spatially aligned. Bear in mind that resolutions can vary wildly and that different modalities are subjected to different transformations and distortions. The idea of this work is to (automatically) identify features that appear in all modalities (such as cracks, canvas, etc.) and use those to perform image registration.

We have a high-resolution multimodal dataset of famous paintings at our disposal that can be used in the context of this work.

The aim of this bachelor thesis is the implementation of an image registration using Sum of Gaussians (SoG).
A 2D-image can be approximated by a sum of 2D-Gaussians. Thus, the image can be represented by a few parameters (position, standard deviation and color of the Gaussians). To compute these parameters a uniform sampling of the image can be used, by placing the Gaussian at the sample points, with a fixed standard deviation and the average color of the surrounding, or a hierarchy can be established using a quad-tree. These heuristics can be improved by minimizing the approximation error (error between the original image and the SoG-image) with respect to the parameters of the Gaussians.
The SoG-representation of the image can be used for several applications, such as tracking or registration of images. Registration of two images is done by minimizing an objective function that measures the overlap and similarity (e.g. color) of the Gaussians of one image and the other image with respect to the translation between the images. This results in a non-linear optimization with two degrees of freedom (translation in x and y direction).
The algorithm should be implemented in C++ under Windows.
This thesis may be written in German or English.


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