Abstract book (pdf) - ICPR 2010

Functional neuroimaging consists in the use of imaging technologies allowing to record the functional brain activity in
real-time. Among all techniques, data produced by functional magnetic resonance is encoded as sequences of 3D images
of thousands of voxels. The main investigation performed on this data, termed brain mapping, aims at producing functional
maps of the brain. Brain mapping aims at the detection of the portion of voxels concerned with specific perceptual or cognitive
brain activities. This challenge can be shaped as a problem of feature selection. Excessive features-to-instances ratio
characterizing this data is a major issue for the computation of statistically robust maps. We propose a solution based on
a Random Subspace Method that extends the reference approach (Search Light) adopted by the neuroscientific community.
A comparison of the two methods is supported by the results of an empirical evaluation.
09:00-11:10, Paper WeAT9.37
Dual Channel Colocalization for Cell Cycle Analysis using 3D Confocal Microscopy
Jaeger, Stefan, Chinese Academy of Sciences
Casas-Delucchi, Corella S., Tech. Univ. Darmstadt
Cardoso, M. Cristina, Tech. Univ. Darmstadt
Palaniappan, Kannappan, Univ. of Missouri
We present a cell cycle analysis that aims towards improving our previous work by adding another channel and using one
more dimension. The data we use is a set of 3D images of mouse cells captured with a spinning disk confocal microscope.
All images are available in two channels showing the chromocenters and the fluorescently marked protein PCNA, respectively.
In the present paper, we will describe our recent colocalization study in which we use Hessian-based blob detectors
in combination with radial features to measure the degree of overlap between both channels. We show that colocalization
performed in such a way provides additional discriminative power and allows us to distinguish between phases that we
were not able to distinguish with a single 2D channel.
09:00-11:10, Paper WeAT9.38
Automated Cell Phase Classification for Zebrafish Fluorescence Microscope Images
Lu, Yanting, Nanjing Univ. of Science and Tech.
Lu, Jianfeng, Nanjing Univ. of Science and Tech.
Liu, Tianming, Univ. of Georgia
Yang,Jingyu, Univ. of Georgia
Automated cell phenotype image classification is an interesting bioinformatics problem. In this paper, an automated cell
phase classification framework is investigated for zebra fish presomitic mesoderm (PSM) images. Low image resolution,
gradual transitions between adjacent categories and irregularity of real cell images make this classification task tough but
intriguing. The proposed framework first segments zebra fish image into cell patches by a two-stage segmentation procedure,
then extracts feature set NF9, which designed especially for this low resolution image set, on each cell patch, and finally
employs support vector machine (SVM) as cell classifier. At present, the total accuracy by NF9 is 75%.
09:00-11:10, Paper WeAT9.39
Data-Driven Lung Nodule Models for Robust Nodule Detection in Chest CT
Farag, Amal, Univ. of Louisville
Graham, James, Univ. of Louisville
Farag, Aly A., Univ. of Louisville
The quality of the lung nodule models determines the success of lung nodule detection. This paper describes aspects of
our data-driven approach for modeling lung nodules using the texture and shape properties of real nodules to form an average
model template per nodule type. The ELCAP low dose CT (LDCT) scans database is used to create the required statistics
for the models based on modern computer vision techniques. These models suit various machine learning approaches
for nodule detection including Bayesian methods, SVM and Neural Networks, and computations may be enhanced through
genetic algorithms and Adaboost. The eminence of the new nodule models are studied with respect to parametric models
showing significant improvements in both sensitivity and specificity.
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