Clinical Image Analysis

A method for the semi-automated segmentation of the meniscus in OAI images developed by the CIALAB Osteoarthritis Research Group has been accepted for publication in the Osteoarthritis and Cartilage Journal. The authors of the paper were Mark Swanson, a Medical Student with Roessler Scholarship, Jeff Prescott, an MD/PhD student in the lab, Thomas Best MD/PhD, Kimberly Powell, Rebecca Jackson MD, Furqan Haq PhD, and Metin Gurcan PhD. The abstract of the manuscript appears below. Abstract: Objective : The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance (MR) images to use for normal knees and those with moderate osteoarthritis (OA). Method : The segmentation method was developed then evaluated on 10 baseline magnetic resonance images obtained from subjects with no evidence, symptoms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). Aft

A method for the segmentation of the quadriceps muscles in OAI images developed by the CIALAB Osteoarthritis Research Group has been accepted for publication in the Journal of Digital Imaging. The authors of the paper were Jeff Prescott, an MD/PhD student in the lab, Thomas Best MD/PhD, Mark Swanson, Furqan Haq PhD, Rebecca Jackson MD, and Metin Gurcan PhD. The abstract of the manuscript appears below. Abstract: In this paper we present a semi-automated segmentation method for magnetic resonance (MR) images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback-Leibler divergence betwee

New research on the meniscus performed by the Osteoarthritis Research Group in the CIALAB and presented at the OARSI conference was highlighted in an article on docguide.com.

CIALAB members Dr. Kamel Belkacem-Boussaid and Jeff Prescott have had their recent work accepted for presentation at SPIE 2010, taking place February 13-18 in San Diego, California. The accepted papers are: 1- K. Belkacem-Boussaid, J. Prescott, G. Lozanski, and M. Gurcan, “ Segmentation of follicular regions on H&E slides using a matching filter and active contour model” ABSTRACT: Follicular Lymphoma (FL) accounts for 20-25% of non-Hodgkin lymphomas in the US. The first step in follicular lymphoma grading is the identification of follicles. The goal of this paper is to develop a technique to segment follicular regions in H&E stained images. The method is based on a robust active contour model in which the centroid of the expanded curve is selected manually by the user. The novel aspect of this method is the introduction of matched filter for the flattening of background in the L channel of the Lab color space. The performance of the algorithm was tested by comparing it against

Dr. Gurcan has been awarded an American Cancer Society Ohio Division Supported Pilot Grant for beginning researchers to engage in basic, clinical, or behavioral/cancer control research. For this project entitled "Computer-aided clinical image analysis for cutaneous lymphomas," Dr. Gurcan will work with Drs. Porcu, Wong and Pennell to develop a system to analyze cutaneous lymphomas. http://www.cancer.org/docroot/COM/content/div_OH/COM_7_2x_Ohio_Grant_Recipients.asp. This project is part of Dr. Gurcan’s ongoing efforts to produce accurate and reproducible tools to distinguish discrete subsets of human cancers and to standardize measurements of response after therapy, which are essential for translational and clinical research.