Clinical Image Analysis

Thesis Title : Computer-Assisted Discovery and Characterization of Imaging Biomarkers for Disease Diagnosis and Treatment Planning Abstract: The rapid growth of diagnostic medical imaging studies has led to enormous strides in the effective diagnosis and treatment of myriad diseases, from chronic diseases to life threatening cancers. The rise of imaging as a major factor in medical decision making has directly led to a drive towards quantification of image findings, in order to augment the qualitative analysis of trained medical professionals, such as radiologists. The overarching goal of this dissertation is to explore, develop, and evaluate imaging biomarkers for both chronic and life threatening diseases. Towards this goal, this dissertation has the following two aims: 1. Discover and characterize imaging biomarkers for diseases which may have either acute/sub-acute presentation or treatment, or diseases which may have a more chronic course and treatment intervention. The former ana...

CIALAB research on the detection of follicles in follicular lymhpoma has been accepted for publication in TBME Letters. Information regarding the publication is found below: Title: Detection of Follicles from IHC Stained Slides of Follicular Lymphoma Using Iterative Watershed Authors: Siddharth Samsi, Gerard Lozanski, Arwa Shana'ah, Ashok K Krishanmurthy, Metin N. Gurcan Abstract: Follicular Lymphoma (FL) is one of the most com- mon types of non-Hodgkin Lymphoma in the United States. Diagnosis of FL is based on tissue biopsy that shows characteristic morphologic and immunohistochemical findings. Our group’s work focuses on development of computer-aided image analysis techniques to improve FL grading. Since centroblast enumeration needs to be performed in malignant follicles, the development of an automated system to accurately identify follicles on digital images of lymphoid tissue is an important step. In this paper we describe an automated system to identify follicles in IHC st...

CIALAB research on developing an automated computer-assisted system for follicular lymphoma grading has been accepted for publication in IEEE Transactions on Biomedical Engineering's forthcoming special issue on Multi-Parameter Optical Imaging and Image Analysis. Information regarding the publication is below: Title: Computer-aided Detection of Centroblasts for Follicular Lymphoma Grading using Adaptive Likelihood based Cell Segmentation Authors: Sertel O, Lozanski G, Shana'ah A, Gurcan MN Abstract: Follicular lymphoma (FL), is one of the most common lymphoid malignancies in the western world. FL has a variable clinical course and important clinical treatment decisions for FL patients are based on histological grading, which is done by manually counting the large malignant cells called centroblasts (CB) in ten standard microscopic high power fields from H&E-stained tissue sections. This method is tedious and subjective; as a result suffers from considerable inter- and in...

Thesis Title: Image Analysis for Computer-Aided Histopathology The main goal of this dissertation is to understand and address the challenges associated with the development of image analysis techniques for the computer-aided interpretation of high-resolution histopathology imagery. We aim to design algorithms for key image analysis tasks such as robust and adaptive segmentation of cytological components for higher level processing, construction of biologically relevant and computationally tractable features and their mathematical representations in order to differentiate distinct tissue sub-types, detection of prognostically significant tissue structures, and spatial alignment of tissue sections prepared with different stains in order to incorporate complementary in-formation. Further information on BioImagene can be found at http://www.bioimagene.com

Two papers have been accepted for oral presentation and inclusion in the Proceedings of 2010 IEEE International Conference on Electro/Information Technology (EIT 2010), to be held at Illinois State University, Normal, Illinois, USA, May 20-22, 2010. Dr. Sufyan Ababneh will be presenting the following two papers at the EIT conference. First paper: An Automated Content-Based Segmentation Framework: Application to MR Images of Knee for Osteoarthritis Research Second paper: An Efficient Graph-Cut Segmentation for Knee Bone Osteoarthritis Medical Images

Recent advances in technological solutions for automated high-speed and high-resolution whole slide imaging (WSI) have set the basis for a digital revolution in microscopy. This ability to observe and analyze entire specimens rather that single microscopic fields of view is affecting the way microscopic evaluation is practiced. However, WSI outputs quite huge multiple channel (at least three color channels) images (e.g. 30-40 GB) for a single slide and managing such amount of data is a unique challenge for this new era of digital microscopy. Currently, WSI workstations are mainly used to perform virtual microscopy, the practice of converting entire glass slides into high-resolution digital slides that can be viewed and managed across networks. The aim of the proposed special issue is to present some of the cutting-edge works currently being done in Whole Slide Imaging and reveal the challenges that still lie ahead. The special issue will be a mix of invited and solicited papers. A pers...

Our work on the segmentation of meniscus is featured in today’s OSU Research News. CIALAB is committed to developing state-of-the art image analysis techniques to help physicians and medical researchers. The article can be read the at the following address: http://researchnews.osu.edu/archive/kneesegment.htm