Abstract
Complex phenotypes are of growing importance in agriculture and medicine. In Zea mays, the most widely produced crop in the world (United States Department of Agriculture. World Agricultural Production. United States Department of Agriculture, Foreign Agricultural Service, Washington, 2015), the disease lesion mimic mutants produce regions of discolored or necrotic tissue in otherwise healthy plants. These mutants are of particular interest due to their apparent action on immune response pathways, providing insight into how plants protect against infectious agents. These phenotypes vary considerably as a function of genotype and environmental conditions, making them a rich, though challenging, phenotypic problem. To segment and quantitate these lesions, we present a novel cascade of adaptive algorithms able to accurately segment the diversity of Z. mays lesions. First, multiresolution analysis of the image allows for salient features to be detected at multiple scales. Next, gradient vector diffusion enhances relevant gradient vectors while suppressing noise. Finally, an active contour algorithm refines the lesion boundary, producing a final segmentation for each lesion. We compare the results from this cascade with manual segmentations from human observers, demonstrating that our algorithm is comparable to humans while having the potential to speed analysis by several orders of magnitude.
Highlights
IntroductionFrom the susceptibility to chronic diseases to the yield of crops when environmentally stressed, understanding complex phenotypes lies at the heart of efforts to better the lives of humans and our planet’s environment
1.1 The impact of phenotypic complexity on image segmentationFrom the susceptibility to chronic diseases to the yield of crops when environmentally stressed, understanding complex phenotypes lies at the heart of efforts to better the lives of humans and our planet’s environment
These images sample from the broad phenotypic variation present among maize lesion mimic mutants, and highlight variation in phenotypic characteristics and the difficulties in measuring them
Summary
From the susceptibility to chronic diseases to the yield of crops when environmentally stressed, understanding complex phenotypes lies at the heart of efforts to better the lives of humans and our planet’s environment. Such phenotypes are difficult to characterize because of their large number of component traits; the wide variation in the components’ values; and the need for large sample sizes to capture the phenotypic responses to the many influencing variables. High throughput imaging can increase sample size, improve quantitation, and better resolve visually related phenotypes, but introduces its own technical issues [2]. Image segmentation is an important task in many medical and biological applications [3,4,5,6]
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