PARALLEL IMAGE PROCESSING FOR HIGH CONTENT SCREENING DATA

Abstract

<p>High-content screening (HCS) produces an immense amount of data, often on the scale of Terabytes. This requires considerable processing power resulting in long analysis time. As a result, HCS with a single-core processor system is an inefficient option because it takes a huge amount of time, storage and processing power. The situation is even worse because most of the image processing software is developed in high-level languages which make customization, flexibility and multi-processing features very challenging. Therefore, the goal of the project is to develop a multithreading model in C language. This model will be used to extract subcellular localization features, such as threshold adjacency statistics (TAS) from the HCS data. The first step of the research was to identify an appropriate dye for use in staining the MCF-7 cell line. The cell line has been treated with staurosporin kinase inhibitor, which can provide important physiological and morphological imaging information. The process of identifying a suitable dye involves treating cells with different dye options, capturing the fluorescent images of the treated cells with the Opera microscope, and analyzing the imaging properties of the stained cells. Several dyes were tested, and the most suitable dye to stain the cellular membrane was determined to be Di4-Anepps. The second part of the thesis was to design and develop a parallel program in C that can extract TAS features from the stained cellular images. The program reads the input cell images captured by Opera microscopes, converts it to TIFF format from the proprietary Opera format, identifies the region-of-interest contours of each cell, and computes the TAS features. A significant increase in speed in the order of four fold was obtained using the customized program. Different scalability tests using the developed software were compared against software developed in Acapella scripting language. The result of the test shows that the computational time is proportional to number of cells in the image and is inversely proportional to number of cores in a processor.</p>