Content-based Microscopic Image Analysis

By (author) Chen Li

Book cover: Content-based Microscopic Image Analysis

Extent: 196, 196 pages

Publisher: Logos Verlag Berlin

Subjects: Earth & Life Sciences , Technical Sciences: Computer Science, Engineering, IT, & Math

Series: Studien zur Mustererkennung

Series volume number: 39

Language: English

Paperback (Published)

(May 2016)

ISBN: 9783832542535

5.71 x 8.27 inches

Price: $55.00

Out of stock

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.

  • By (author) Chen Li