Abstract book (pdf) - ICPR 2010

and non text pixels. Our previous Laplacian method is employed on AoA for text detection. The proposed method is evaluated
by testing on a large dataset which includes publicly available data, non text data and ICDAR-03 data. Comparative
study with existing methods shows that the results of the proposed method are encouraging and useful.
ThBT7 Dolmabahçe Hall C
Quantitative Biological Image and Signal Analysis Regular Session
Session chair: Tasdizen, Tolga (Univ. of Utah)
13:30-13:50, Paper ThBT7.1
Improving Undersampled MRI Reconstruction using Non-Local Means
Adluru, Ganesh, Univ. of Utah
Tasdizen, Tolga, Univ. of Utah
Whitaker, Ross, Univ. of Utah
Dibella, Edward, Univ. of Utah
Obtaining high quality images in MR is desirable not only for accurate visual assessment but also for automatic processing
to extract clinically relevant parameters. Filtering-based techniques are extremely useful for reducing artifacts caused due
to under sampling of k-space (to reduce scan time). The recently proposed Non-Local Means (NLM) filtering method
offers a promising means to denoise images. Compared to most previous approaches, NLM is based on a more realistic
model of images, which results in little loss of information while removing the noise. Here we extend the NLM method
for MR image reconstruction from under sampled k-space data. The method is applied on T1-weighted images of the
breast and T2-weighted anatomical brain images. Results show that NLM offers a promising method that can be used for
accelerating MR data acquisitions.
13:50-14:10, Paper ThBT7.2
Towards an Intelligent Bed Sensor: Non-Intrusive Monitoring of Sleep Irregularities with Computer Vision Techniques
Branzan Albu, Alexandra, Univ. of Victoria
Malakuti, Kaveh, Univ. of Victoria
This paper proposes a novel approach for monitoring sleep using pressure data. The goal of sleep monitoring is to detect
and log events of normal breathing, sleep apnea and body motion. The proposed approach is based on translating the signal
data to the image domain by computing a sequence of inter-frame similarity matrices from pressure maps acquired with
a mattress of pressure sensors. Periodicity analysis was performed on similarity matrices via a new algorithm based on
segmentation of elementary patterns using the watershed transform, followed by aggregation of quasi-rectangular patterns
into breathing cycles. Once breathing events are detected, all remaining elementary patterns aligned on the main diagonal
are considered as belonging to either apnea or motion events. The discrimination between these two events is based on
detecting movement times from a statistical analysis of pressure data. Experimental results confirm the validity of our approach.
14:10-14:30, Paper ThBT7.3
Automatic Selection of Keyframes from Angiogram Videos
Syeda-Mahmood, Tanveer, IBM Almaden Res. Center
Wang, Fei, Almaden Res. Center
Beymer, David, IBM Almaden Res. Center
Mahmood, Aafreen, Monta Vista High School
Lundstrom, Robert, Kaiser Permanente SFO Medical Center
In this paper we address the problem of automatic selection of important vessel-depicting key frames within 2D angiography
videos. Two different methods of frame selection are described, one based on Frangi filter, and the other based on
detecting parallel curves formed from edges in angiography images. Results are shown by comparison to physician annotation
of such key frames on 2D coronary angiograms.
14:30-14:50, Paper ThBT7.4
A Computer-Aided Method for Scoliosis Fusion Level Selection by a Topologicaly Ordered Self Organizing Kohonen
Network
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