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Recognition of micro-array protein crystals images using multi-scale representations

Ya Wang; David H. Kim; Elsa D. Angelini; Andrew F. Laine

Title:
Recognition of micro-array protein crystals images using multi-scale representations
Author(s):
Wang, Ya
Kim, David H.
Angelini, Elsa D.
Laine, Andrew F.
Date:
Type:
Articles
Department:
Biomedical Engineering
Permanent URL:
Book/Journal Title:
Medical imaging 2005 : Image processing : 13-17 February 2005, San Diego, California, USA ; Proceedings of SPIE, vol. 5747
Book Author:
Fitzpatrick, J. Michael
Publisher:
SPIE
Publisher Location:
Bellingham, Wash.
Abstract:
Micro-array protein crystal images are now routinely acquired automatically by CCD cameras. High-throughput automatic classification of protein crystals requires to alleviation of the time-consuming task of manual visual inspection. We propose a classification framework combined with a multi-scale image processing method for recognizing protein crystals and precipitates versus clear drops. The main two points of the processing method are the multi-scale Laplacian pyramid filters and histogram analysis techniques to find an effective feature vector. The processing steps include: 1. Tray well cropping using Radon Transform; 2. Droplet cropping using an ellipsoid Hough Transform; 3. Multi-scale image separation with Laplacian pyramidal filters; 4. Feature vector extraction from the histogram of the multi-scale boundary images. The feature vector combines geometric and texture features of each image and provides input to a feed forward binomial neural network classifier. Using human (expert crystallographers) classified images as ground truth, the current experimental results gave 86% true positive and 94% true negative rates (average true percentage is 90%) using an image database which contained over 2,000 images. To enable NESG collaborators to carry our crystal classification, a web-based Matlab server was also developed. Users at other locations on the internet can input micro-array crystal image folders and parameters for training and testing processes through a friendly web interface. Recognition results are shown on the client side website and may be downloaded by a remote user as an Excel spreadsheet file.
Subject(s):
Biomedical engineering
Publisher DOI:
10.1117/12.595902
Item views:
252
Metadata:
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