Robust regression and outlier detection
Robust regression and outlier detection
Fundamentals of digital image processing
Fundamentals of digital image processing
Probability and statistics for the engineering, computing, and physical sciences
Probability and statistics for the engineering, computing, and physical sciences
Detection and characterization of isolated and overlapping spots
Computer Vision and Image Understanding
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Early Visual Learning
A Pyramid Framework for Early Vision: Multiresolutional Computer Vision
A Pyramid Framework for Early Vision: Multiresolutional Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spotting Approaches for Biochip Arrays
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Application of computerized image processing in functional genomics: preliminary results
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Recognition of perspectively distorted planar grids
Pattern Recognition Letters
Microarchitecture of a multicore SoC for data analysis of a lab-on-chip microarray
EURASIP Journal on Advances in Signal Processing
Computational Statistics & Data Analysis
Expression microarray classification using topic models
Proceedings of the 2010 ACM Symposium on Applied Computing
Sub-grid detection in DNA microarray images
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Implementing automatic spot addressing and contour based segmentation in microarray image analysis
BEBI'08 Proceedings of the 1st WSEAS international conference on Biomedical electronics and biomedical informatics
Biclustering of expression microarray data using affinity propagation
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
A comparison on score spaces for expression microarray data classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
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DNA microarrays are an increasingly important tool that allow biologists to gain insight into the function of thousands of genes in a single experiment. Common to all array-based approaches is the necessity to analyze digital images of the scanned DNA array. The ultimate image analysis goal is to automatically quantify every individual array element (spot), providing information about the amount of DNA bound to a spot. Irrespective of the quantification strategy, the preliminary information to extract about a spot includes the mapping between its location in the digital image and its possibly distorted position in the spot array (gridding). We present a gridding approach divided into a spot-amplification step (matched filter), a rotation estimation step (Radon transform), and a grid spanning step. Quantification of the spots is performed by robustly fitting of a parametric model to pixel intensities with the help of M-estimators. The main advantage of parametric spot fitting is its ability to cope with overlapping spots. If the goodness-of-fit is too bad, a semiparametric spot fitting is employed. We show that our approach is superior to simple quantification strategies such as averaging of the pixel intensities. The system was extensively tested on 1740 images resulting from two DNA libraries.