Abstract book (pdf) - ICPR 2010

separate atlas, and identifying the most suitable templates to be used as coordinate reference frames. The spectral analysis
step relies on pairwise distances that express anatomical differences between subjects as a function of the diffeomorphic
warp required to match the one subject onto the other, plus residual information. The methodology is validated numerically
on artificial and medical imaging data.
09:00-11:10, Paper WeAT9.17
Automatic Pathology Annotation on Medical Images: A Statistical Machine Translation Framework
Gong, Tianxia, National Univ. of Singapore
Li, Shimiao, National Univ. of Singapore
Tan, Chew-Lim, National Univ. of Singapore
Pang, Boon Chuan, National Neuroscience Inst. Tan Tock Seng Hospital
Lim, Tchoyoson, National Neuroscience Inst. Tan Tock Seng Hospital
Lee, Cheng Kiang, National Neuroscience Inst. Tan Tock Seng Hospital
Tian, Qi, Insitute of Infocomm Res.
Zhang, Zhuo, Insitute of Infocomm Res.
Large number of medical images are produced daily in hospitals and medical institutions, the needs to efficiently process,
index, search and retrieve these images are great. In this paper, we propose a pathology based medical image annotation
framework using a statistical machine translation approach. After pathology terms and regions of interest (ROIs) are extracted
from training text and images respectively, we use machine translation model IBM Model 1 to iteratively learn the
alignment between the ROIs and the pathology terms and generate an ROI-to-pathology translation table. In testing phase,
we annotate the ROI in the image with the pathology label of the highest probability in the translation table. The overall
annotation results and the retrieval performance are promising to doctors and medical professionals.
09:00-11:10, Paper WeAT9.18
3D Cell Nuclei Fluorescence Quantification using Sliding Band Filter
Quelhas, Pedro, INEB- Inst. de Engenharia Biomedica
Mendonça, Ana Maria, INEB - Inst. de Engenharia Biomédica
Aurélio, Campilho, Faculdade de Engenharia da Univ. do Porto
Plant development is orchestrated by transcription factors whose expression has become observable in living plants through
the use of fluorescence microscopy. However, the exact quantification of expression levels is still not solved and most
analysis is only performed through visual inspection. With the objective of automating the quantification of cell nuclei
fluorescence we present a new approach to detect cell nuclei in 3D fluorescence confocal microscopy, based on the use of
the sliding band convergence filter (SBF). The SBF filter detects cell nuclei and estimate their shape with high accuracy
in each 2D image plane. For 3D detection, individual 2D shapes are joined into 3D estimates and then corrected based on
the analysis of the fluorescence profile. The final nuclei detection’s precision/recall are of 0.779/0.803 respectively, and
the average Dice’s coefficient of 0.773.
09:00-11:10, Paper WeAT9.19
AP-Based Consensus Clustering for Gene Expression Time Series
Chiu, Tai-Yu, National Tsing Hua Univ.
Hsu, Ting-Chieh, National Tsing Hua Univ.
Wang, Jia-Shung, National Tsing Hua Univ.
We propose an unsupervised approach for analyzing gene time-series datasets. Our method combines Affinity Propagation
(AP) and the spirit of consensus clustering— extracting multiple partitions from different time intervals. Without priori
knowledge of total number of clusters and exemplars, this method holds the relationship between genes through different
time intervals, and eliminates the influence from noises and outliers. We demonstrate our method with both synthetic and
real gene expression datasets showing significant improvement in accuracy and efficiency.
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