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> 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 <http://www.gmu.edu/resources/welcome/Directions-to-GMU.html>
>   
> *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.
>
>
> ABSTRACT:
> 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.
>
>   ###
>