Zheng, Lei
(2006)
AUTOMATED FEATURE EXTRACTION AND CONTENT-BASED RETRIEVAL OFPATHOLOGY MICROSCOPIC IMAGES USING K-MEANS CLUSTERING AND CODE RUN-LENGTH PROBABILITY DISTRIBUTION.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
The dissertation starts with an extensive literature survey on the current issues in content-based image retrieval (CBIR) research, the state-of-the-art theories, methodologies, and implementations, covering topics such as general information retrieval theories, imaging, image feature identification and extraction, feature indexing and multimedia database search, user-system interaction, relevance feedback, and performance evaluation. A general CBIR framework has been proposed with three layers: image document space, feature space, and concept space. The framework emphasizes that while the projection from the image document space to the feature space is algorithmic and unrestricted, the connection between the feature space and the concept space is based on statistics instead of semantics. The scheme favors image features that do not rely on excessive assumptions about image contentAs an attempt to design a new CBIR methodology following the above framework, k-means clustering color quantization is applied to pathology microscopic images, followed by code run-length probability distribution feature extraction. Kulback-Liebler divergence is used as distance measure for feature comparison. For content-based retrieval, the distance between two images is defined as a function of all individual features. The process is highly automated and the system is capable of working effectively across different tissues without human interference. Possible improvements and future directions have been discussed.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
31 January 2006 |
| Date Type: |
Completion |
| Defense Date: |
31 October 2005 |
| Approval Date: |
31 January 2006 |
| Submission Date: |
16 December 2005 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Information Sciences > Information Science |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
color quantization; image retrieval; information retrieval; medical imaging |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-12162005-124025/, etd-12162005-124025 |
| Date Deposited: |
10 Nov 2011 20:11 |
| Last Modified: |
15 Nov 2016 13:54 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/10394 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}