Invited speakers

 Prof. Maria Petrou
Imperial College, London , U.K

• A new imaging architecture and a challenge to the neuro-
  physiologists

Artificial imaging systems imitate our understanding of the biological ones: the continuous world is sampled at regular or irregular points and the samples are used as the pixels, the building blocks of the captured image. A lot of image processing, however, relies on continuous methods, which inevitably are applied to the captured samples. It will be argued that the information required by the image processing and pattern recognition modules of vision may be extracted directly from the scene as opposed to be calculated from the digital samples, by using appropriately constructed sensors.

 Prof. Peter Sturm
INRIA, Grenoble, Rhone-Alpes, France

• 3D and appearance modeling from images

Approaches for generating photorealistic 3D models from images will be presented. Works by the presenter as well as an overview of the general state of the art will be explained. The talk will concentrate on multi-view stereo but aspects such as photometric stereo and reconstruction of non-diffuse object appearance models, will be covered.

 Prof. Walter G. Kropatsch
Vienna University of Technology, Viena

• When pyramids learned walking

A temporal image sequence increases the dimension of the data by simply stacking images above each other. This further raises the computational complexity of the processes. The typical content of a pixel or voxel is its grey or color value. With some processing features and fitted model parameters are added. In a pyramid these values are repeatedly summarized in the stack of images or image descriptions with a constant factor of reduction. From this derives their efficiency of allowing log(diameter) complexity for global information transmission. Content propagates bottom-up by reduction functions like inheritance or filters. Content propagates top-down by expansion functions like interpolation or projection. Moving objects occlude different parts of the image background. Computing one pyramid per frame needs lots of bottom-up computation and very complex and time consuming updating. In the new concept we propose one pyramid per object and one pyramid for the background. The connection between both is established by coordinates that are coded in the pyramidal cells much like in a Laplacian pyramid or a wavelet. We envision that this code will be stored in each cell and will be invariant to the basic movements of the object. All the information about position and orientation of the object is concentrated in the apex. New positions are calculated for the apex and can be accurately reconstructed for every cell in a top-down process. At the new pixel locations the expected content can be verified by comparing it with the actual image frame.

 Prof.  Ioannis A. Kakadiaris
Computational Biomedicine Lab Depts. of CS, ECE, and Biomedical Engineering, U. of Houston

• Challenges and Opportunities for Extracting Cardiovascular Risk Biomarkers from non-contrast CT data

In this talk, I will first offer a short overview of the research activities of the Computational Biomedicine Laboratory, University of Houston. Then, I will present our research in the area of biomedical image computing for the mining of information from cardiovascular imaging data for the detection of persons with a high likelihood of developing a heart attack in the near future (vulnerable patients). Specifically, I’ll present methods for detection and segmentation of anatomical structures, and shape and motion estimation of dynamic organs. The left ventricle in non-invasive cardiac MRI data is extracted using a new multi-class, multi-feature fuzzy connectedness method and deformable models for shape and volume estimation. In non-invasive cardiac CT data, the thoracic fat is detected using a relaxed version of multi-class, multi-feature fuzzy connectedness method. Additionally, the calcified lesions in the coronary arteries are identified and quantified using a hierarchical supervised learning framework from the CT data. In non-invasive contrast-enhanced CT, the coronary arteries are detected using our tubular shape detection method for motion estimation and, possibly, for non-calcified lesion detection. In invasive IVUS imaging, our team has developed a unique IVUS acquisition protocol and novel signal/image analysis methods for the detection (for the first time in-vivo) of ‘vasa vasorum’ (VV). The VV are micro-vessels that are commonly present to feed the walls of larger vessels; however, recent clinical evidence has uncovered their tendency to proliferate around areas of inflammation, including the inflammation associated with vulnerable plaques. In summary, our work is focused on developing innovative computational tools to mine quantitative parameters from imaging data for early detection of asymptomatic cardiovascular patients. The expected impact of our work stems from the fact that sudden heart attack remains the number one cause of death in the US, and unpredicted heart attacks account for the majority of the $280 billion burden of cardiovascular diseases.