Offline and Online Interactive Frameworks for MRI and CT Image Analysis in the Healthcare Domain : The Case of COVID-19, Brain Tumors and Pancreatic Tumors
| dc.contributor.advisor | Alhajj, Reda S. | |
| dc.contributor.author | Sailunaz, Kashfia | |
| dc.contributor.committeemember | Alhajj, Reda S. | |
| dc.contributor.committeemember | Rokne, Jon George | |
| dc.contributor.committeemember | Ozyer, Tansel | |
| dc.contributor.committeemember | Kawash, Jalal Yusef | |
| dc.contributor.committeemember | Agarwal, Nitin | |
| dc.date | 2023-11 | |
| dc.date.accessioned | 2023-08-24T16:37:10Z | |
| dc.date.available | 2023-08-24T16:37:10Z | |
| dc.date.issued | 2023-08 | |
| dc.description.abstract | Medical imaging represents the organs, tissues and structures underneath the outer layers of skin and bones etc. and stores information on normal anatomical structures for abnormality detection and diagnosis. In this thesis, tools and techniques are used to automate the analysis of medical images, emphasizing the detection of brain tumor anomalies from brain MRIs, Covid infections from lung CT images and pancreatic tumor from pancreatic CT images. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning models, and recent deep learning models are used. The following problems for medical images : abnormality detection, abnormal region segmentation, interactive user interface to represent the results of detection and segmentation while receiving feedbacks from healthcare professionals to improve the analysis procedure, and finally report generation, are considered in this research. Complete interactive systems containing conventional models, machine learning, and deep learning methods for different types of medical abnormalities have been proposed and developed in this thesis. The experimental results show promising outcomes that has led to the incorporation of the methods for the proposed solutions based on the observations of the performance metrics and their comparisons. Although currently separate systems have been developed for brain tumor, Covid and pancreatic cancer, the success of the developed systems show a promising potential to combine them to form a generalized system for analyzing medical imaging of different types collected from any organs to detect any type of abnormalities. | |
| dc.identifier.citation | Sailunaz, K. (2023). Offline and online interactive frameworks for MRI and CT image analysis in the healthcare domain : the case of COVID-19, brain tumors and pancreatic tumors (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | |
| dc.identifier.uri | https://hdl.handle.net/1880/116889 | |
| dc.identifier.uri | https://dx.doi.org/10.11575/PRISM/41731 | |
| dc.language.iso | en | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject.classification | Education--Health | |
| dc.subject.classification | Education--Technology | |
| dc.subject.classification | Computer Science | |
| dc.title | Offline and Online Interactive Frameworks for MRI and CT Image Analysis in the Healthcare Domain : The Case of COVID-19, Brain Tumors and Pancreatic Tumors | |
| dc.type | doctoral thesis | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Doctor of Philosophy (PhD) | |
| ucalgary.thesis.accesssetbystudent | I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible. |