[log in to unmask]" type="cite">Dissertation Defense Announcement:
To:  The George Mason University Community

Candidate: Mekhala Acharya
Program: PhD Bioinformatics & Computational Biology 

Date:   Tuesday October 23, 2012
Time:   12:00 - 2:00 P.M. 
Place:  George Mason University
 	     Occoquan Bldg. Room #110-L
	     Prince William campus
Dissertation Director: Dr. Jason Kinser
Committee Members: Dr. Donald Seto, Dr. Jeffrey Solka, Dr. Geraldine Grant

Title: "Image Analysis of Radiological Images from Patients with Advanced Lung Disease" 

The dissertation is on reserve in the Johnson Center Library, Fairfax campus.
The doctoral project will not be read at the meeting, but should be read in advance. 
All members of the George Mason University community are invited to attend.

There are diagnostic challenges in the evaluation of a patient with a known or suspected diagnosis of diffuse infiltrative lung disease  or interstitial lung disease [ILD] because of the extensive possibilities of diverse potential diagnoses with similar symptoms. High-Resolution Computed Tomography (HRCT) has changed the diagnostic evaluation of patients with ILD and is particularly useful in the diagnosis of idiopathic pulmonary fibrosis (IPF). The characteristic HRCT findings of IPF are reticular abnormality and honeycombing with basal and peripheral predominance and the radiographic pattern differs with the stage of the disease.

The quantification of disease by CT is important to indicate prognosis and to evaluate progression of the disease or response to treatment. It is difficult to convey the complex textural information offered by a CT scan hindered by the lack of user friendly technology for image analysis. Automated tools are presented which extract information from the CT images and isolate visual evidence of the disease from healthy lung tissue.  Each CT image is converted to a set of pulse images, which through collective synchronization of pixels extract pertinent information of the diseased regions. These pulse streams are used for training and recall through an associative memory so that entire images can be segmented.  

In spite of the obvious difference in contrast, volume and texture, the healthy and diseased regions are distinguished and classified using pulse images.  The technique used is successful in classifying the healthy and diseased portions of the lung. The goal is to train adequate and varied stages of IPF images and to be able to extract sufficiently enough information from test images. The algorithm was tested on HRCT scans procured through INOVA Fairfax Hospital, Department of Radiology. Two expert radiological reviewers compared the initial results of the segmentation algorithm with the manual segmentation of the original scans. Comparison revealed agreement regarding the presence or absence of honeycombing. Algorithms and results for the analysis of patients with IPF and healthy patients are presented.

The absence of gold standards in image processing makes quantification challenging for early stage images of IPF and blinded images. Thus medical image processing validation often cannot rely on availability of true gold standards. Hence lung volumes derived from Pulmonary Function Tests (PFT) results served as established clinical parameters and were used as "gold standards" . The results of the segmentation were compared with the measurements of the pulmonary function tests. The relationship between image segmentation results and the PFT results were calculated using linear aggression analysis and Pearson's product moment correlation. Volumetric measurements of  of honeycomb, vascular and normal regions are found to correlate with results of PFTs in patients with IPF. The greatest correlation was between honeycomb regions and forced vital capacity (FVC). The healthy and honeycomb regions correlated negatively with PFT measure diffusing capacity (DLco).  Results demonstrate that the segmentation of IPF images using PCNN techniques are useful in extracting quantitative information.