An image analysis system (IAS) was developed for the quantitative assessment of estrogen receptor’s (ER) positive status from breast tissue microscopy images. Twenty-four cases of breast cancer biopsies, immunohistochemically (IHC) stained for ER, were microscopically assessed by a histopathologist, following a clinical routine scoring protocol. Digitized microscopy views of the specimens were ...

Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of st...

Automated image analysis of cells and tissues has been an active research field in medical informatics for decades but has recently attracted increased attention due to developments in computer and microscopy hardware and the awareness that scientific and diagnostic pathology require novel approaches to perform objective quantitative analyses of cellular and tissue specimens. Model-based approa...

This paper presents an efficient and innovative system for automated classification of periodontal diseases, The strength of our technique lies in the fact that it incorporates knowledge from the patients' clinical data, along with the features automatically extracted from the Haematoxylin and Eosin (H&E) stained microscopic images. Our system uses image processing techniques based on color dec...

Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as intricate task histopathology image analysis. Segmenting a nucleus important part diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate tell apart independent nuclei. Deep Learning swiftly paving its way arena segmentation, attracting quite few researchers w...