An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium.
BACKGROUND
The 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.


RESULTS
The 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.


CONCLUSIONS
This 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.
Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.
Marek
M
Schikora
Balram
B
Neupane
Satish
S
Madhogaria
Wolfgang
W
Koch
Daniel
D
Cremers
Heribert
H
Hirt
Karl-Heinz
KH
Kogel
Adam
A
Schikora
D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000276:immunology* / Q000382:microbiology*; D058947:Bacterial Secretion Systems; D004926:Escherichia coli / Q000472:pathogenicity; D054884:Host-Pathogen Interactions / Q000276:immunology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010935:Plant Diseases / Q000276:immunology* / Q000382:microbiology*; D057865:Plant Immunity; D018515:Plant Leaves / Q000276:immunology / Q000382:microbiology; D044224:Pseudomonas syringae / Q000472:pathogenicity; D012486:Salmonella typhimurium / Q000235:genetics / Q000472:pathogenicity*
D058947:Bacterial Secretion Systems