Event
M.S. Thesis Defense: Sai Deepika Regani
Tuesday, May 9, 2017
10:00 a.m.
Room 2460, AVW
Maria Hoo
301 405 3681
mch@umd.edu
ANNOUNCEMENT: M.S. Thesis Defense
Name: Sai Deepika Regani
Committee:
Professor Rama Chellappa
Professor Behtash Babadi
Professor Joseph F. Jaja
Date/Time: Tuesday, May 9th, 2017 at 10:00 am
Place: Room 2460, AVW
Title: 3D MULTIMODAL IMAGE REGISTRATION: Application to equine PET and CT images.
Abstract:
Positron Emission Tomography (PET) is being widely used in the veterinary medicine in the recent years. Although it was limited to small animals because of its classical design and the large amount of radionuclide doses required, PET imaging in horses became possible with the introduction of a portable PET scanner developed by Brain Biosciences Inc. It was observed that this new modality could capture abnormalities like lesions that Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and other modalities could not. Since 2016, PET imaging in horses is being studied and analysed.
While PET provides functional information characterizing the activity of lesions, it is useful to combine information from other modalities like CT and match the structural information to develop an accurate spatial representation of the data. Since biochemical changes occur much earlier than structural changes, this helps detect lesions and tumors during the early stages. Multi-modal image registration is used to achieve this goal. A series of steps are proposed to automate the process of registration of equine PET and CT images. Multi-modal image registration using landmark based and intensity based techniques are studied. It is observed that a few tissues are not imaged in the PET, which makes image segmentation, an important preprocessing step in the registration process. A study of the segmentation algorithms relevant to the field of medical imaging is presented. The performance of the segmentation algorithms improved with the extent of manual interaction and intensity based registration gave the smallest time complexity with reasonable accuracy.