Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper WeAT9.6
Human State Classification and Predication for Critical Care Monitoring by Real-Time Bio-Signal Analysis
Li, Xiaokun, DCM Res. Res. LLC
Porikli, Fatih, MERL
To address the challenges in critical care monitoring, we present a multi-modality bio-signal modeling and analysis modeling
framework for real-time human state classification and predication. The novel bioinformatic framework is developed
to solve the human state classification and predication issues from two aspects: a) achieve 1:1 mapping between the biosignal
and the human state via discriminant feature analysis and selection by using probabilistic principle component
analysis (PPCA); b) avoid time-consuming data analysis and extensive integration resources by using Dynamic Bayesian
Network (DBN). In addition, intelligent and automatic selection of the most suitable sensors from the bio-sensor array is
also integrated in the proposed DBN.
09:00-11:10, Paper WeAT9.7
Automated Cephalometric Landmark Identification using Shape and Local Appearance Models
Keustermans, Johannes, K.U. Leuven
Mollemans, Wouter, Medicim nv.
Vandermeulen, Dirk
Suetens, Paul, K.U.Leuven
In this paper a method is presented for the automated identification of cephalometric anatomical landmarks in craniofacial
cone-beam CT images. This method makes use of statistical models, incorporating both local appearance and shape knowledge
obtained from training data. Firstly, the local appearance model captures the local intensity pattern around each
anatomical landmark in the image. Secondly, the shape model contains a local and a global component. The former improves
the flexibility, whereas the latter improves the robustness of the algorithm. Using a leave-one-out approach to the
training data, we assess the overall accuracy of the method. The mean and median error values for all landmarks are equal
to 2.55mm and 1.72mm, respectively.
09:00-11:10, Paper WeAT9.8
Color Analysis for Segmenting Digestive Organs in VCE
Vu, Hai, The Inst. of Scientific and Industrial Res. Osaka
Echigo, Tomio, Osaka Electro-Communication Univ.
Yagi, Yasushi, Osaka Univ.
Yagi, Keiko, Kobe Pharmaceutical Univ.
Shiba, Masatsugu, Osaka City Univ.
Higuchi, Kazuhide, Osaka City Univ.
Arakawa, Tetsuo, Osaka City Univ.
This paper presents an efficient method for automatically segmenting the digestive organs in a Video Capsule Endoscopy
(VCE) sequence. The method is based on unique characteristics of color tones of the digestive organs. We first introduce
a color model of the gastrointestinal (GI) tract containing the color components of GI wall and non-wall regions. Based
on the wall regions extracted from images, the distribution along the time dimension for each color component is exploited
to learn the dominant colors that are candidates for discriminating digestive organs. The strongest candidates are then
combined to construct a representative signal to detect the boundary of two adjacent regions. The results of experiments
are comparable with previous works, but computation cost is more efficient.
09:00-11:10, Paper WeAT9.9
A New Application of Meg and DTI on Word Recognition
Meng, Lu, Northeastern Univ.
Xiang, Jing, CCHMC
Zhao, Hong, Northeastern Univ.
Zhao, Dazhe, Northeastern Univ.
This paper presented a novel application of Magneto encephalography (MEG) and diffusion tensor image (DTI) on word
recognition, in which the spatiotemporal signature and the neural network of brain activation associated with word recognition
were investigated. The word stimuli consisted of matched and mismatched words, which were visually and acousti-
- 184 -