ArticleCrystal growth & design2013
Real-Time Protein Crystallization Image Acquisition and Classification System.
Article in Crystal growth & design, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Deep learning applications in protein crystallography.Acta crystallographica. Section A, Foundations and advances · 2024Review
- Article
- Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images.Journal of fluorescence · 2020Article
- Feature analysis for classification of trace fluorescent labeled protein crystallization images.BioData mining · 2017Article
- Optimizing Associative Experimental Design for Protein Crystallization Screening.IEEE transactions on nanobioscience · 2016Article
- Automation in biological crystallization.Acta crystallographica. Section F, Structural biology communications · 2014Article
- Autofocusing for Microscopic Images using Harris Corner Response Measure.Proceedings of IEEE Southeastcon. IEEE Southeastcon · 2014Article
- Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images.Proceedings of IEEE Southeastcon. IEEE Southeastcon · 2014Article
- Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.Proceedings of IEEE Southeastcon. IEEE Southeastcon · 2014Article
- Super-Thresholding: Supervised Thresholding of Protein Crystal Images.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Schema Matching and Data Integration with Consistent Naming on Protein Crystallization Screens.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- FocusALL: Focal Stacking of Microscopic Images Using Modified Harris Corner Response Measure.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- CrystPro: Spatiotemporal Analysis of Protein Crystallization Images.Crystal growth & designArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this paper, we describe the design and implementation of a stand-alone real-time system for protein crystallization image acquisition and classification with a goal to assist crystallographers in scoring crystallization trials. In-house assembled fluorescence microscopy system is built for image acquisition. The images are classified into three categories as non-crystals, likely leads, and crystals. Image classification consists of two main steps - image feature extraction and application of classification based on multilayer perceptron (MLP) neural networks. Our feature extraction involves applying multiple thresholding techniques, identifying high intensity regions (blobs), and generating intensity and blob features to obtain a 45-dimensional feature vector per image. To reduce the risk of missing crystals, we introduce a max-class ensemble classifier which applies multiple classifiers and chooses the highest score (or class). We performed our experiments on 2250 images consisting 67% non-crystal, 18% likely leads, and 15% clear crystal images and tested our results using 10-fold cross validation. Our results demonstrate that the method is very efficient (< 3 seconds to process and classify an image) and has comparatively high accuracy. Our system only misses 1.2% of the crystals (classified as non-crystals) most likely due to low illumination or out of focus image capture and has an overall accuracy of 88%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.