Content-based Histopathology Image Retrieval using a CometCloud-based infrastructure

Project Details

Project Lead
Javier Diaz Montes 
Project Manager
Javier Diaz Montes 
Project Members
Ivan Rodero, Manuel Diaz-Granados, Esma Yıldırım  
Institution
Rutgers, The State University of New Jersey, Rutgers Discovery Informatics Institute (RDI2) / NSF Center for Cloud and Autonomic Computing (CAC)  
Discipline
Radiology (715) 
Subdiscipline
---613 Pathology--- 

Abstract

 

Research in content-based image retrieval (CBIR) has emerged as an important focus of investigation multiple image-related disciplines. We explore a broad spectrum of potential clinical applications in pathology with a newly developed set of retrieval algorithms that were fine-tuned for each class of digital pathology images, including peripheral blood smears, mammary glands, glomeruli (kidney) and Hematoxylin-stained breast tissue microarray (TMA). We utilized the CometCloud autonomic cloud engine to run the CBIR algorithms in parallel across federated resources.
 

Intellectual Merit

The goal of this research is to develop algorithms and evaluate them on multiple histopathology image datasets. Moreover, a computational framework will be developed to orchestrate the workflow of the algorithms and the computational resources.

Broader Impacts

The combination of this techniques and the computational framework will allow doctors to quickly detect possible pathologies in patients.

Scale of Use

We will mainly use VMs to run our experiments