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

Preserving data replication consistency through ROWA-MSTS

In modern distributed systems, replication receives particular aware�ness to provide high data availability, reliability and enhance the performance of the system. Replication becomes as significant mechanism since it enables organizations to provide users with admission to current data where and when they need it. Integrated VSFTPD with Read-One-Write-All Monitoring Syn�chronization Transaction System (ROWA-MSTS) has been developed to moni�tor data replication and transaction performs in distributed system environment. This paper presents the ROWA-MSTS framework and process flow in order to preserve the data replication consistency. The implementation shows that ROWA-MSTS able to monitor the replicated data distribution while maintain�ing the data consistency over multiple sites

Tumor Region Localization in H&E Breast Carcinoma Images Using Deep Convolutional Neural Network

Digital pathology incorporates the acquisition, management, sharing and interpretation of pathology information in a digital environment. The field of digital pathology is currently regarded as one of the most promising avenues of diagnostic medicine. Many computer-aided detection and diagnostic algorithms has been developed to assist pathologists in their daily clinical routine, with varying degree of success. These include cell detection and counting, tissue classification and cancer grading, among others. Deep learning, or more specifically, deep convolutional neural network, is a machine learning algorithm that has also gained a lot of attention recently due to their ability to achieve state-of-the-art accuracy. In this paper we have constructed and expanded the deep model network to localize tumor regions in histology images of breast carcinoma. We proposed our own deep convolutional neural network with lesser hardware requirement using 64×64×3 input patch. Our proposed method is able to provide reliable tumor region localization, visually and objectively, based on very limited training dataset

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