An image classification approach to analyze the suppression of plant immunity by the human pathogen <it>Salmonella</it> Typhimurium

<p>Abstract</p> <p>Background</p> <p>The enteric pathogen <it>Salmonella</it> is the causative agent of the majority of food-borne bacterial poisonings. Resent research revealed that colonization of plants by <it>Salmonella</it> is an active infection process. <it>Salmonella</it> 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 <it>Salmonella</it> infection on <it>Arabidopsis</it>.</p> <p>Results</p> <p>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 <it>E. coli</it> and the plant pathogen <it>Pseudomonas syringae</it> and used to study the interaction between plants and <it>Salmonella</it> wild type and T3SS mutants. We proved that T3SS mutants of <it>Salmonella</it> are unable to suppress the plant defenses. Results obtained through the automatic analyses were further verified on biochemical and transcriptome levels.</p> <p>Conclusion</p> <p>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 <it>Salmonella</it> 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.</p