Program NVPHBV Spring Meeting 2011 (2011)

heart disease is one of the leading causes of death worldwide [1], and coronary artery disease (CAD) is the main cause of coronary heart disease. Therefore, there is much interest in finding biomarkers that could be used for risk stratification and prediction of future events in the patients suspected of CAD [2]. One such biomarker for CAD is the amount of calcium present in the coronary artery plaques, i.e. the calcium score. Whereas risk stratification is currently based on the calcium score for the whole heart, the individual artery calcium score is also of clinical importance, and may be relevant for better diagnosis and therapy planning. We present an automatic coronary artery specific calcium scoring system for non-contrast enhanced cardiac CT images. Since the coronary arteries are not visible on CT images, our method makes use of patient-specific coronary artery soft masks to detect calcium spots close to the location of the arteries. These masks were computed on contrast enhanced CTA atlas images and then mapped onto the CT image [3]. The information from these masks along with other image features were used to design a pattern recognition classification system that is able to detect calcium within the individual coronary arteries. We report all the commonly used calcium scores (agatston, mass and volume)[4,5,6] for the arteries and the whole heart. The results of the classification system (both, the number of calcification and the total score) are compared to those obtained by manual observers. REFERENCES [1] “Cardiovascular diseases (cvds) fact sheet n317,” World Health Organization, 2011. [2] R. Wayhs, A. Zelinger, and P. Raggi, “High coronary artery calcium scores pose an extremely elevated risk for hard events,” J Am Coll Cardiol, vol. 39, pp. 225–230, 2002. [3] R. Shahzad, M. Schaap, T. van Walsum, S.Klein, A.C. Weustink, L.J. van Vliet, W. J. Niessen, “A Patient Specific Artery Density Estimate, ” Proceedings of IEEE International Symposium on Biomedical Imaging: from Nano to Macro, pp. 9-12, 2010. [4] AS Agatston, WR Janowitz, FJ Hildner, NR Zusmer, M. Viamonte Jr, and R. Detrano, “Quantification of coronary artery calcium using ultrafast computed tomography,” Journal of the American College of Cardiology, vol. 15, no. 4, pp. 827, 1990. [5] T. Callister, B. Cooil, S. Raya, N. Lippolis, D. Russo, and P. Raggi. Coronary artery disease: improved reproducibility of calcium scoring with an electron beam CT volumetric Radiology, vol. 208, pp. 807–814, 1998. [6] Hong C, Becker CR, Schoepf UJ, Ohnesorge B, Bruening R, Reiser MF. artery calcium: absolute quantification in non-enhanced and contrast-enhanced multi–detector row CT studies. Radiology, vol. 223, pp. 474–480, 2002. Automatic detection of retinal vascular bifurcations by trainable V4-like filters George Azzopardi and Nicolai Petkov Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, the Netherlands e-mail: {g.azzopardi, n.petkov}@rug.nl We propose a novel method for the detection of vascular bifurcations in retinal fundus images. Our method is implemented in trainable filters that simulate the functions of shape-selective neurons in area V4 of visual cortex. The configuration of such a filter is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. In such an analysis, a filter is configured by combining given channels of a bank of Gabor filters, which describe the dominant orientations in the concerned feature, in an AND-gate-like operation. Consequently, the filter can detect the same and similar bifurcation patterns. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images available in the DRIVE public dataset. This performance is sufficient for the requirements of the concerned medical application. Shadow Removal Based on Inpainting and Gradient Manipulation Bart Liefers, Robby T. Tan Multimedia and Geometric Group Department of Information and Computing Sciences Utrecht University e-mail: robby@cs.uu.nl A method for shadow removal based on a gradient manipulation and image inpainting technique is proposed. The method effectively removes variances in color and intensity due to varying illumination. This ability is useful to remove cast shadows, since shadows represent exactly the same variances. Following the algorithm of an existing method, a one-dimensional representation of a shadow-free image is constructed, and used to locate shadow edges. Subsequently, the proposed method employs an inpainting technique in the gradient domain to remove these edges. Integration of the gradient image leads to a new image, similar to the original, but without shadows. Image Processing in Mathematica 8