Doctoral Dissertations

Automatic detection of lesion border and edge-related structures in dermoscopy images

Author

Xiaohe Chen

Keywords and Phrases

Dermoscopy

Abstract

"Early detection of malignant melanoma is critical because early stage diagnosis results in a higher survival rate. As pre-processing steps of digital dermoscopy images in an automatic diagnosis system, line structure identification and lesion border identification algorithms are important for accuracy of diagnosis. This work presents the methodology and procedures to process dermoscopy images using computer vision and data mining methods. A watershed-based adaptive skin lesion border finder was developed and implemented for dermoscopy images...An improved SharpRazor algorithm was developed to remove hairs from dermoscopy images, based on a previously existing DullRazorʼ algorithm for hair removal...Work is also reported here on classifiers for detecting atypical pigment network in skin lesions based on texture"--Abstract, page iv.

Advisor(s)

Moss, Randy Hays, 1953-

Committee Member(s)

Beetner, Daryl G.
Samaranayake, V. A.
Stanley, R. Joe
Shrestha, Bijaya

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Publisher

University of Missouri--Rolla

Publication Date

Fall 2007

Journal article titles appearing in thesis/dissertation

  • Skin lesion segmentation by an adaptive watershed flooding approach
  • Adaptive morphological line structure segmentation on skin lesion images - SharpRazor, a software improvement of DullRazorʼ
  • Detection of atypical texture features in early malignant melanoma

Pagination

xi, 83 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2007 Xiaohe Chen, All rights reserved.

Document Type

Dissertation - Citation

File Type

text

Language

English

Subject Headings

Image processingMelanoma -- DiagnosisSkin -- Cancer -- Diagnosis

Thesis Number

T 9315

Print OCLC #

238657664

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