Real-Time Protein Crystallization Image Acquisition and Classification System

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%.

Figure 1

Layout of assembled microscopy system

Figure 2

Basic flowchart of protein image acquisition process

Figure 3

Sample images under non-crystal category

Figure 4

Sample images under likely leads category

Figure 5

Sample images under crystals category

Figure 6

Flowchart of the integrated algorithm for feature extraction

Figure 7

a, c, and e: median-filtered images; b, d, and f: the Otsu thresholded images for a, c, and e, respectively

Figure 8

Results of showing the application of the 3 thresholding techniques for a sample image. a) Original b) Otsu’s threshold c) 90th per threshold d) Max green threshold

Figure 9

Separating background and foreground region. a) M (Image after noise removal) b) Binary image c) Background pixels d) Foreground pixels

Figure 10

a) M (image after noise removal) b) Binary image, B c) O:Objects (regions) using connected component labeling d) Ω: the skeletonization of object

Figure 11

a) Boundary uniformity b) Measure of symmetry

Figure 12

Precision-recall plot (NC: non-crystal; LL: likely leads; C: crystal, O: Overall) a) Using MLP classifier with all features b) Using max-class ensemble method

Figure 13

Crystals classified as non-crystal using max-class ensemble classifier