> 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.
> 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
> 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