Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

AUTOMATED FEATURE EXTRACTION AND CONTENT-BASED RETRIEVAL OFPATHOLOGY MICROSCOPIC IMAGES USING K-MEANS CLUSTERING AND CODE RUN-LENGTH PROBABILITY DISTRIBUTION

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 (Unpublished)

[img]
Preview
PDF
Primary Text

Download (2MB) | Preview

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:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zheng, Leilzheng@sis.pitt.edu
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMunro, Paulpmunro@mail.sis.pitt.eduPWM
Committee MemberForan, Daviddjf@pleiad.umdnj.edu
Committee MemberKarimi, Hassanhkarimi@mail.sis.pitt.eduHKARIMI
Committee MemberGilbertson, Johngilbertsonjr@msx.upmc.edu
Committee MemberBecich, Michaelbecichmj@upmc.edu
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 View Item