Image analytics for medical imaging and forensic applications

dc.contributor.advisorAgaian, Sos
dc.contributor.authorRajendran, Rahul
dc.contributor.committeeMemberLee, Junghee
dc.contributor.committeeMemberLee, Wonjun
dc.date.accessioned2024-02-12T19:52:22Z
dc.date.available2018-08-19
dc.date.available2024-02-12T19:52:22Z
dc.date.issued2016
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractCancer is currently the second leading cause of death worldwide becoming a clear public health problem, both in developed and developing countries. Kidney cancer, also known as Renal cancer, is among the 10 most common cancers in both men and women. Image analysis plays a crucial role in the field of medicine, as the analysis guides the radiologist towards perfect diagnosis and treatment planning. At times, for faster acquisition, the medical imaging system that captures these tumors are of poor quality and contain a lot of distortions. Hence, enhancing an image to obtain better quality and preserving key features is an important issue in medical image processing. However, there does not exist a Computer Aided Classification (CAC) framework for the classification of renal cancer, and there is limited information about enhancing and segmenting kidney cancer images. The goal of this thesis is to develop an image processing tool to enhance and segment medical and forensic images for the betterment of analysis. The key contributions of this thesis are four fold. First, we propose two enhancement techniques to enhance medical images for better analysis of renal cancer. We also propose a color correction technique to match touchless finger images. Second, we present a segmentation method to extract the tumor regions of the kidney. Third, Particle Swarm Optimization technique is used to extract the nuclei from histopathology images. This technique is further used in the elimination of background in fingerprint images. Finally, we analyze the histopathology images and generate an automated imaging system that classifies such images as benign and malignant.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent87 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/5205
dc.languageen
dc.subject.classificationElectrical engineering
dc.titleImage analytics for medical imaging and forensic applications
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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