Abhishek Vahadane

TL;DR: A large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries is introduced, whose quality was validated by a medical doctor.

TL;DR: Stain density correlation with ground truth and preference by pathologists were higher for images normalized using the method when compared to other alternatives, and a computationally faster extension of this technique is proposed for large whole-slide images that selects an appropriate patch sample instead of using the entire image to compute the stain color basis.

TL;DR: A novel color normalization technique to bring a histological image into a different color appearance of a second image, which therefore standardizes the color representation of both images and preserves the structural information of the source image after colornormalization, which is very important for subsequent image analysis.

TL;DR: Color normalization can give a small incremental benefit when a super-pixel-based classification method is used with features that perform implicit color normalization while the gain is higher for patch- based classification methods for classifying epithelium versus stroma.

TL;DR: Nuclear segmentation was significantly improved on histological images (H&E stained breast and intestinal tissue images, Feulgen stained images of prostate tissues) and seeded watershed segmentation is reported to be a simple and computationally efficient segmentation technique.