An Integrated Content and Metadata based Retrieval System for Art

In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type

Cells Detection and Segmentation in ER-IHC Stained Breast Histopathology Images

In this paper, we present our recent work on cells detection and segmentation in estrogen receptor immunohistochemistry (ER-IHC)-stained breast carcinoma images. The proposed cell detection and segmentation is very useful in the predictive scoring of hormone receptor status in ER-IHC stained whole-slide images, which helps pathologists to decide whether a patient should be offered hormonal therapy or other treatments. The proposed method is based on deep convolutional neural network, followed by watershed-based post-processing. The cell detection results are compared and evaluated objectively against the ground truth provided by our collaborating pathologists. The cell segmentation results, on the other hand, are evaluated visually by overlaying the computer segmented boundaries on the ER-IHC images for comparison. The automated cell detection algorithm recorded precision and recall rates of 95% and 91% respectively. The very promising performances for both the detection and segmentation paves the way for an automated system for hormone receptor scoring in ER-IHC stained whole-slide breast carcinoma image