Cancer Colony Identification VIA Digital Image Processing, Barry Dunn, Gavin Mantica, Jessica Jensen, Joshua Kruzan, Juliette Caffrey

 Title: Cancer Colony Identification VIA Digital Image Processing

Authors:

Barry Dunn, Gavin Mantica, Jessica Jensen, Joshua Kruzan, Juliette Caffrey

University affiliation: Roger Williams University
Department: School of Engineering, Computing and Construction Management

Abstract: 

In an effort to provide a standardized method of quantification for researchers to develop treatments for lung cancer, which is the most lethal form of cancer, a numerical analysis method was created for preclinical use, in which it will accurately and affordably quantify the presence of cancer colonies on mouse lungs in an automated fashion. Using digital image processing, two different algorithms were developed to examine whole lungs and their cross sections. Once samples of the cancerous samples are obtained and photographed; the algorithm would be utilized. It starts by pixelating the photos and converting them to grayscale, before applying filters and other threshold driven image processing techniques, in order to present quantitative analysis on the presence of cancer colonies. The principal result of this project is to have created a method that has less error and can be completed faster than those that currently exist.

Submitted by: Barry Dunn
Major: Engineering
Degree being pursued: BS
Type of student: Senior