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. 2014 Mar:2014:10.1109/SECON.2014.6950744.
doi: 10.1109/SECON.2014.6950744.

Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images

İmren Dinç  1 , Madhav Sigdel  1 , Semih Dinç  1 , Madhu S Sigdel  1 , Marc L Pusey  2 , Ramazan S Aygün  1
Affiliations

Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images

İmren Dinç et al. Proc IEEE Southeastcon. 2014 Mar.

Abstract

In this paper, we investigate the performance of classification of protein crystallization images captured during protein crystal growth process. We group protein crystallization images into 3 categories: noncrystals, likely leads (conditions that may yield formation of crystals) and crystals. In this research, we only consider the subcategories of noncrystal and likely leads protein crystallization images separately. We use 5 different classifiers to solve this problem and we applied some data preprocessing methods such as principal component analysis (PCA), min-max (MM) normalization and z-score (ZS) normalization methods to our datasets in order to evaluate their effects on classifiers for the noncrystal and likely leads datasets. We performed our experiments on 1606 noncrystal and 245 likely leads images independently. We had satisfactory results for both datasets. We reached 96.8% accuracy for noncrystal dataset and 94.8% accuracy for likely leads dataset. Our target is to investigate the best classifiers with optimal preprocessing techniques on both noncrystal and likely leads datasets.

Keywords: classification; normalization; principal component analysis; protein crystallization.

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Figures

Fig. 1
Fig. 1
Clear Drop
Fig. 2
Fig. 2
Regular Granular Precipitate
Fig. 3
Fig. 3
Granular Precipitate or Microcrystals
Fig. 4
Fig. 4
Unclear Bright Regions
Fig. 5
Fig. 5
Effects of data normalization on classifiers for noncrystal dataset
Fig. 6
Fig. 6
Effects of data normalization on classifiers for likely leads dataset
See this image and copyright information in PMC

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