Work

Acceleration of Two Point Correlation Function Calculation for Pathology Image Segmentation

Year: 2011

Type: conference-paper

Abstract: The segmentation of tissue regions in high-resolution microscopy is a challenging problem due to both the size and appearance of digitized pathology sections. The two point correlation function (TPCF) has proved to be an effective feature to address the textural appearance of tissues. However the calculation of the TPCF functions is computationally burdensome and often intractable in the gigapixel images produced by slide scanning devices for pathology application. In this paper we present several approaches for accelerating deterministic calculation of point correlation functions using theory to reduce computation, parallelization on distributed systems, and parallelization on graphics processors. Previously we show that the correlation updating method of calculation offers an 8-35× speedup over frequency domain methods and decouples efficient computation from the select scales of Fourier methods. In this paper, using distributed computation on 64 compute nodes provides a further 42× speedup. Finally, parallelization on graphics processors (GPU) results in an additional 11-16× speedup using an implementation capable of running on a single desktop machine.

Source: IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology

DOI: 10.1109/hisb.2011.10

Authors: Lee A.D. Cooper, Joel H. Saltz, Umit Catalyurek, Kun Huang

Institutions: Emory University, The Ohio State University

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of Mental Health, U.S. National Library of Medicine

Awards: G2175790810, G814052603