An image classification approach to analyze the suppression of plant immunity by the human pathogen SalmonellaTyphimurium

BMC Bioinformatics

Research Article

Daniel Cremers¹  Satish Madhogaria²  Wolfgang Koch²  Marek Schikora³  Adam Schikora⁴  Karl-Heinz Kogel⁴  Balram Neupane⁴  Heribert Hirt⁵ 

[1] Computer Science Department, Technical University of Munich, 85748, Garching, Germany;Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343, Wachtberg, Germany;Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343, Wachtberg, Germany;Computer Science Department, Technical University of Munich, 85748, Garching, Germany;Institute for Plant Pathology and Applied Zoology, IFZ, JL University Giessen, 35392, Giessen, Germany;URGV Plant Genomics, INRA/CNRS/University d’Evry, 97000, Evry, France;

关键词: Support Vector Machine;    Gaussian Mixture Model;    Arabidopsis Plant;    Hypersensitive Response;    Support Vector Machine Classifier;   

DOI  :  10.1186/1471-2105-13-171

 received in 2012-01-31, accepted in 2012-05-11,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundThe enteric pathogen Salmonella is the causative agent of the majority of food-borne bacterial poisonings. Resent research revealed that colonization of plants by Salmonella is an active infection process. Salmonella changes the metabolism and adjust the plant host by suppressing the defense mechanisms. In this report we developed an automatic algorithm to quantify the symptoms caused by Salmonella infection on Arabidopsis.ResultsThe algorithm is designed to attribute image pixels into one of the two classes: healthy and unhealthy. The task is solved in three steps. First, we perform segmentation to divide the image into foreground and background. In the second step, a support vector machine (SVM) is applied to predict the class of each pixel belonging to the foreground. And finally, we do refinement by a neighborhood-check in order to omit all falsely classified pixels from the second step. The developed algorithm was tested on infection with the non-pathogenic E. coli and the plant pathogen Pseudomonas syringae and used to study the interaction between plants and Salmonella wild type and T3SS mutants. We proved that T3SS mutants of Salmonella are unable to suppress the plant defenses. Results obtained through the automatic analyses were further verified on biochemical and transcriptome levels.ConclusionThis report presents an automatic pixel-based classification method for detecting “unhealthy” regions in leaf images. The proposed method was compared to existing method and showed a higher accuracy. We used this algorithm to study the impact of the human pathogenic bacterium Salmonella Typhimurium on plants immune system. The comparison between wild type bacteria and T3SS mutants showed similarity in the infection process in animals and in plants. Plant epidemiology is only one possible application of the proposed algorithm, it can be easily extended to other detection tasks, which also rely on color information, or even extended to other features.

【 授权许可】

CC BY   
© Schikora et al.; licensee BioMed Central Ltd. 2012