Classification of Protein Crystallisation Images Using Texture-Based Statistical Features

被引：3

作者：

Lekamge, B. M. Thamali ^{[1
,2
]}

Sowmya, Arcot ^{[1
]}

Mele, Katarina ^{[2
]}

Fazio, Vincent J. ^{[3
]}

Newman, Janet ^{[3
]}

机构：

[1] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia

[2] CSIRO, Riverside Life Sci Ctr, Computat Informat Div, N Ryde, NSW 2113, Australia

[3] CSIRO, Mat Sci & Engn, Parkville, Vic 3052, Australia

来源：

2013 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MODELS FOR LIFE SCIENCES | 2013年 / 1559卷

关键词：

Protein crystallisation; texture based features; classification; decision trees;

D O I：

10.1063/1.4825019

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We report on the classification of protein crystallisation data via decision trees using N-fold cross validation. Protein crystallisation images were obtained over a period of time ranging from days to months. All the images taken of a single experiment are arranged according to the time they were obtained and aligned according to the position of the experimental droplet. Difference between consecutive images in a time course was determined and the background noise was removed using filtering techniques. Image analysis is performed on the area inside the droplet of each difference image to compute statistical texture features. Classification is carried out by testing and training with 1000 sample images and an accuracy rate of similar to 75% was achieved.

引用

页码：270 / 276

页数：7

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