Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper WeAT8.22
On the Design of a Class of Odd-Length Biorthogonal Wavelet Filter Banks for Signal and Image Processing
Baradarani, Aryaz, Univ. of Windsor
Mendapara, Pankajkumar, Univ. of Windsor
Wu, Q. M. Jonathan, Univ. of Windsor
In this paper, we introduce an approach to the design of odd-length biorthogonal wavelet filter banks based on semi definite
programming employing Bernstein polynomials. The method is systematic and renders a simple optimization problem,
yet it offers wavelet filters ranging from maximally flat to maximal passband/stopband width. The odd-length biorthogonal
filter pairs are then used in multi-focus imaging to obtain a fully-focused image from a set of registered semi-focused
input images at varying focus employing the distance transform and exponentially decaying function on the subbands in
wavelet domain. Various images are tested and experimental results compare favorably to recent results in literature.
09:00-11:10, Paper WeAT8.23
Implicit Feature-Based Alignment System for Radiotherapy
Yamakoshi, Ryoichi, Mitsubishi Electric Corp.
Hirasawa, Kousuke, Mitsubishi Electric Corp.
Okuda, Haruhisa, Mitsubishi Electric Corp.
Kage, Hiroshi, Mitsubishi Electric Corp.
Sumi, Kazuhiko, Mitsubishi Electric Corp.
Ivanov, Yuri, MERL, USA
Sakamoto, Hidenobu, Mitsubishi Electric Corp.
Yanou, Toshihiro, Hyogo Ion Bean Medical Center, Tokyo
Suga, Daisaku, Hyogo Ion Bean Medical Center, Tokyo
Murakami, Masao, Hyogo Ion Bean Medical Center, Tokyo
In this paper we present a robust alignment algorithm for correcting the effects of out-of-plane rotation to be used for automatic
alignment of the Computed Tomography (CT) volumes and the generally low quality fluoroscopic images for radiotherapy
applications. Analyzing not only in-plane but also out-of-plane rotation effects on the Dignitary Reconstructed
Radiograph (DRR) images, we develop simple alignment algorithm that extracts a set of implicit features from DRR.
Using these SIFT-based features, we align DRRs with the fluoroscopic images of the patient and evaluate the alignment
accuracy. We compare our approach with traditional techniques based on gradient-based operators and show that our algorithm
performs faster while in most cases delivering higher accuracy.
09:00-11:10, Paper WeAT8.24
3D Vertebrae Segmentation in CT Images with Random Noises
Aslan, Melih Seref, Univ. of Louisville
Ali, Asem, Univ. of Louisville
Farag, Aly A., Univ. of Louisville
Arnold, Ben, Image Analysis, Inc
Chen, Dongqing, Univ. of Louisville
Ping, Xiang, Image Analysis, Inc.
Exposure levels (X-ray tube amperage and peak kilovoltage) are associated with various noise levels and radiation dose.
When higher exposure levels are applied, the images have higher signal to noise ratio (SNR) in the CT images. However,
the patient receives higher radiation dose in this case. In this paper, we use our robust 3D framework to segment vertebral
bodies (VBs) in clinical computed tomography (CT) images with different noise levels. The Matched filter is employed
to detect the VB region automatically. In the graph cuts method, a VB (object) and surrounding organs (background) are
represented using a gray level distribution models which are approximated by a linear combination of Gaussians (LCG).
Initial segmentation based on the LCG models is then iteratively refined by using Markov Gibbs random field(MGRF)
with analytically estimated potentials. Experiments on the data sets show that the proposed segmentation approach is more
accurate and robust than other known alternatives.
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