- Title: Automatic Registration of Large Images from Light Microscopy
- Description: In recent decades, interest in computer vision has increased dramatically. This can be attributed to computers managing large volumes of data and an increasing focus on this field. It is only recently that it has become possible to apply ideas from computer vision in powerful computer systems. In the biomedical research field, an important tool in characterisation of tissue morphology and pathology is microscopic imaging. In this thesis samples of lung tissue, provided by Medetect, are used in the registration application. The lung tissue samples are sliced into extremely thin sections and stained with different markers. Each section is scanned in an Aperio ScanScope slide digitiser, resulting in extremely large images. This preparation process (i.e. sectioning, staining, cover glass application) can introduce a variety of deformations, including bending, stretching and tearing. However, when the images are not aligned identically this can sometimes cause difficulty at the analysis stage. Although this mismatch of consecutive sections can be corrected manually to facilitate their study, this procedure can be extremely time-consuming and an automatic process is an interesting development. The registration process is divided into four main steps: pre-processing, feature extraction, classification and image alignment. The pre-processing step involves suitable operations, which have been shown to greatly improve the registration process. Two main approaches are used to extract keypoints, SIFT and MSER. The keypoints are then matched to produce point correspondences. The alignment process uses the RANSAC algorithm to estimate a rigid transformation using point correspondences. Registration is then complete and hopefully characterisation of tissue morphology and pathology will have been facilitated considerably. Results show that registration using SIFT has produced relatively good results and that it is very important to pre-process the images beforehand. However, when using both SIFT and MSER the matches can be boosted to some degree.
- Start Date: Aug. 1, 2009
- Finished Date: Feb. 26, 2010
- Supervisor: Petter Strandmark
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Last update: 2013-04-11
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