Cellular Wall Segmentation of A. Thaliana Epidermal Tissue in DIC Images

In this work, we discuss the problems of automatic segmentation of Arabidopsis thaliana epidermal cell patterning in images captured using a Differential Interference Contrast (DIC) microscope. These images are difficult to analyze due to the non-linear nature of DIC optics. The border of objects appear as a combination of bright and dark shadows with variable thickens and contrast levels. Our segmentation approach exploits prior knowledge on the optical properties of A. thaliana cell walls. A set of matching filters and a scale space line detector are used to generate an enhanced image that shows a single response at the location of cellular walls. To reduce the uncertainty in low contrast cellular walls several images are captured at different orientations. After image fusion, we obtain a single image that can be segmented using well established algorithms. Experiments on a manually annotated DIC image data set demonstrated the effectiveness of the proposed scheme.