Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper WeAT9.13
Statistical Texture Modeling for Medical Volume using Generalized N-Dimensional Principal Component Analysis
Method and 3D Volume Morphing
Qiao, Xu, Ritsumeikan Univ.
Chen, Yen-Wei, Ritsumeikan Univ.
In this paper, a statistical texture modeling method is proposed for medical volumes. As the shapes of the human organ
are very different from one case to another, 3D volume morphing is applied to normalize all the volume datasets to a same
shape for removing shape variations. In order to deal with the problems of high-dimension and small number of medial
samples, we propose an effective image compression method named Generalized N-dimensional Principal Component
Analysis (GND-PCA) to construct a statistical model. Experiments applied on liver volumes show good performance on
generalization using our method. A simple experiment is employed to show that the features extracted by the statistical
texture model have capability of discrimination for different types of data, such as normal and abnormal.
09:00-11:10, Paper WeAT9.14 CANCELED
Distinguishing Patients with Gastritis and Cholecystitis from the Healthy by Analyzing Wrist Radial Arterial Doppler
Blood Flow Signals
Jiang, Xiaorui, Harbin Inst. of Tech.
Zhang, Dongyu, Harbin Inst. of Tech.
Wang, Kuanquan, Harbin Inst. of Tech.
Zuo, Wangmeng, Harbin Inst. of Tech.
This paper tries to fill the gap between Traditional Chinese Pulse Diagnosis (TCPD) and Doppler diagnosis by applying
digital signal analysis and pattern classification techniques to wrist radial arterial Doppler blood flow signals. Doppler
blood flows signals (DBFS) of patients with cholecystitis, gastritis and healthy people are classified by L2-soft margin
SVM and 5 linear classifiers using the proposed feature - piecewise axially integrated bispectra (PAIB). A 5-fold cross
validation is used for performance evaluation. The classification accuracies between either two groups of subjects are
greater than 93%. Gastritis can be recognized with higher accuracy than cholecystitis. Cholecystitis can be recognized
with higher accuracy on left hand data than right. The findings in this paper partly conform to the theory of TCPD. Though
the sample size is relatively small, we could still argue that the methods proposed here are effective and could serve as an
assistive tool for TCPD.
09:00-11:10, Paper WeAT9.15
Pelvic Organs Dynamic Features Analysis for MRI Sequences Discrimination
Rahim, Mehdi, Univ. Paul Cézanne
Bellemare, Marc-Emmanuel, Univ. Paul Cézanne
Pirro, Nicolas, Hôpital La Timone
Bulot, Rémy, Univ. Paul Cézanne
Dynamic magnetic resonance imaging MRI acquisitions are used in the clinical assessment of the pelvic organs behaviour
during an abdominal strain. The main organs (bladder, uterus-vagina, rectum) undergo deformations and intrinsic movements
along a sequence. Anatomical references and measurements are generally used by clinicians to evaluate pathology
grades. In this context, we have established quantitative elements, which consist of deformation and movement features,
for the pelvic dynamic characterization, by using shape descriptors computed from organ contours. Moreover, the deformation
and movement features relevance has been assessed towards an efficient sequence discrimination and pathology
detection.
09:00-11:10, Paper WeAT9.16
Multiple Atlas Inference and Population Analysis with Spectral Clustering
Sfikas, Giorgos, Univ. of Ioannina
Heinrich, Christian, Univ. de Strasbourg
Nikou, Christophoros, Univ. of Ioannina
In medical imaging, constructing an atlas and bringing an image set in a single common reference frame may easily lead
the analysis to erroneous conclusions, especially when the population under study is heterogeneous. In this paper, we propose
a framework based on spectral clustering that is capable of partitioning an image population into sets that require a
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