Doctoral Dissertations


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


Xiaohe Chen

Keywords and Phrases



"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.


Moss, Randy Hays, 1953-

Committee Member(s)

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


Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering


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


xi, 83 pages

Note about bibliography

Includes bibliographical references.


© 2007 Xiaohe Chen, All rights reserved.

Document Type

Dissertation - Citation

File Type




Subject Headings

Image processing
Melanoma -- Diagnosis
Skin -- Cancer -- Diagnosis

Thesis Number

T 9315

Print OCLC #


Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

This document is currently not available here.

Share My Dissertation If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the button above.