Content-based Microscopic Image Analysis

by Chen Li

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.

196 pages, Paperback

Published May 15, 2016

About the author

Chen Li (born 1954) was raised in Hualien, Taiwan and has published over ten books of poetry. Regarded as “one of the most innovative and exciting poets writing in Chinese today” (Michelle Yeh, University of California at Davis), he is also a prolific prose writer and translator.
Chen Li studied and taught English in his native town of Hualian on the east coast of Taiwan, from which he has never moved. Taking part in this year's Poetry International Festival has been his first trip abroad. In an article entitled 'The Traveller' he once observed: 'So long as I keep longing for the outside world, I'm always underway.