Histopathology Images

Colour Normalisation in Digital Histopathology Images

Colour consistency in light microscopy based histology is an increasingly important problem with the advent of Gigapixel digital slide scanners and automatic image analysis. This paper presents an evaluation of two novel colour normalisation approaches against the previously utilised method of linear normalisation in lαβ colourspace. These approaches map the colour distribution of an over/under...

Automatic cancer detection and localization on prostatectomy histopathology images

Transfer Learning for Cell Nuclei Classification in Histopathology Images

In histopathological image assessment, there is a high demand to obtain fast and precise quantification automatically. Such automation could be beneficial to find clinical assessment clues to produce correct diagnoses, to reduce observer variability, and to increase objectivity. Due to its success in other areas, deep learning could be the key method to obtain clinical acceptance. However, the ...

Automated Segmentation of Nuclei in Breast Cancer Histopathology Images

The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentati...

A Semantic Content-Based Retrieval Method for Histopathology Images

This paper proposes a model for content-based retrieval of histopathology images. The most remarkable characteristic of the proposed model is that it is able to extract high-level features that reflect the semantic content of the images. This is accomplished by a semantic mapper that maps conventional low-level features to high-level features using state-of-the-art machine-learning techniques. ...