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Title: Content-based Microscopic Image Analysis

Author: Dr.-Ing. Chen Li

Supervisor: Prof. Dr. Marcin Grzegorzek

Reviewers: Prof. Dr. Marcin Grzegorzek and Prof. Dr. Klaus-Dieter Kuhnert

Date of defence: 16.02.2016

Evaluation: 1.0 (full score) pass, Very Good (MAGNA CUM LAUDE)

In this dissertation, novel Content-based Microscopic Image Analysis (CBMIA) methods, including Weakly Supervised Learning (WSL), are proposed to aid biological studies. In a CBMIA task, noisy image, image rotation, and object recognition problems need to be addressed. To this end, the  first approach  is  a  general  supervised  learning method, which consists of image segmentation, shape feature extraction, classification, and feature fusion, leading to a semi-automatic approach. In contrast, the second approach  is  a  WSL  method,  which  contains Sparse  Coding (SC)  feature  extraction, classification, and feature fusion, leading to a full-automatic approach.  In this WSL approach, the problems of noisy image and object recognition are jointly resolved by a region-based classifier, and the image rotation problem is figured out through SC features. To demonstrate the usefulness and potential of the proposed methods, experiments are implemented on different practical biological tasks, including environmental microorganism classification, stem cell analysis, and insect tracking.