Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification.

Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM).

Charged domains in ferroelectric, polycrystalline yttrium manganite thin films resolved with scanning electron microscopy.

We have investigated ferroelectric charged domains in polycrystalline hexagonal yttrium manganite thin films (YMnO, YMnO, YMnTiO, and YMnTiO) by scanning electron microscopy (SEM) in secondary electron emission mode with a small acceleration voltage. Using SEM at an acceleration voltage of 1.0 kV otherwise homogenous surface charging effects are reduced, polarization charges can be observed and polarization directions (±P) of the ferroelectric domains in the polycrystalline thin films can be identified.