Multiscale digital images and high throughput experiments: challenges, results and perspectives
Place: Room 144, block B, Cidade Universitária | City: São Paulo, Brazil
Experimental science is facing a hundred to thousand-fold increase in data volumes, and manual interaction with every record is unfeasible. This talk will report on quantitative analysis techniques using digital images, by means of automated computer algorithms (Quant-CT) developed at LBNL. We use mathematical models to describe structures, develop algorithms to represent pore network, and compare observations from material samples with simulations. Together, they lead to new paradigms for image representation and visualization. We will overview a material science problem, describe the implementation, and demonstrate the application of Quant-CT on microtomography. In addition, we will check how schemes used in Quant-CT can be extended to interpret other 3D imaging modalities such as MRI and electron microscopy.
Dani Ushizima is the Deputy Head of the Visualization Group, and a scientist at the Computational Research Division, LBNL. Previously, she was a professor at the Catholic University of Santos, Brazil, supported by FAPESP under the Brazilian Young Researcher Award. She has collaborated with high technology groups in universities and companies in Brazil since 1996, focusing on the application of pattern recognition to several media, particularly digital images in medicine, material science and geology. She received her PhD in Computational Physics from the Physics Institute, USP in 2004, where she developed a prototype for computer-aided leukemia diagnosis using multivariable statistics, and feature selection tools for multi-purpose data applications. In 2004, she was also a Visiting Researcher in the Electrical and Computer Engineering Department at UC Santa Barbara. She received her B.S. in Computer Science from the Federal University of Sao Carlos, Brazil, while working on automation of Intensive Care Unit procedures in collaboration with industry (Dixtal Biomedica and Albert Einstein Institute). Her research interests include machine vision, image analysis, pattern recognition, quantitative microscopy and statistical analysis.