Mekhala Acharya, Jason M. Kinser, Steven Nathan, Marcia C. Albano, and Lori Schlegel
High resolution computed tomography, Idiopathic pulmonary fibrosis, Pulse coupled neural network, Intersecting cortical model, Fast analog associative memory
CT images of patients diagnosed with idiopathic pulmonary fibrosis (IPF) present visual evidence of the dis- ease. 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 and analyzed. This work presents the algorithms and results for the analysis of patients with IPF and normal patients. Results demonstrate that segmentation of IPF images is useful in extracting quantitative information.
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