University of Houston
Lancaster, Keith C.
Asymmetry Measures for Automated Melanoma Detection in Dermoscopic Images.
Degree: PhD, Electrical Engineering, 2018, University of Houston
Dermoscopic rules such as the ABCD and Menzies rules are employed by dermatologists to determine the likelihood that a suspicious lesion is cancerous. This dissertation focuses on the improvement of automated melanoma recognition systems that implement these rules, speciﬁcally by enhancing the ability of these systems to recognize lesion asymmetry, a signiﬁcant indicator of melanoma.
Two approaches are proposed for asymmetry classiﬁcation. The ﬁrst utilizes the irregularity of the outer contour of the lesion combined with measures that compare quadrants of the lesion with respect to area, color, and melanin content. The second method uses size theory as the basis for determining asymmetry. In this approach, measuring functions are employed to expose relevant characteristics of the lesion. The one-dimensional measuring functions are mapped into size functions in R 2 and compared using the bottleneck distance. The distances are used as features for classiﬁcation.
Annotated dermoscopic images were used to train classiﬁers for both methods. Classiﬁcation rates were competitive with other approaches for both methods independently, with the combined method exhibiting 95% accuracy.
Additionally, decision fusion strategies were investigated as a means of combining the results from individual melanoma classiﬁers using the asymmetry methods developed in this study. The best approach showed 100% sensitivity and 64% speciﬁcity, exceeding the performance of the individual classiﬁers.
Finally, a software framework for the development of medical applications is presented. This framework attempts to provide biomedical researchers with a simpliﬁed approach to creating mobile applications for medical processing.
Advisors/Committee Members: Zouridakis, George (advisor), Chen, Ji (committee member), Jansen, Ben H. (committee member), Glover, John R. (committee member), Yuan, Xiaojing (committee member).
Subjects/Keywords: Image processing; Smartphone; Melanoma; Automated melanoma recognition
to Zotero / EndNote / Reference
APA (6th Edition):
Lancaster, K. C. (2018). Asymmetry Measures for Automated Melanoma Detection in Dermoscopic Images. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/3137
Chicago Manual of Style (16th Edition):
Lancaster, Keith C. “Asymmetry Measures for Automated Melanoma Detection in Dermoscopic Images.” 2018. Doctoral Dissertation, University of Houston. Accessed November 30, 2020.
MLA Handbook (7th Edition):
Lancaster, Keith C. “Asymmetry Measures for Automated Melanoma Detection in Dermoscopic Images.” 2018. Web. 30 Nov 2020.
Lancaster KC. Asymmetry Measures for Automated Melanoma Detection in Dermoscopic Images. [Internet] [Doctoral dissertation]. University of Houston; 2018. [cited 2020 Nov 30].
Available from: http://hdl.handle.net/10657/3137.
Council of Science Editors:
Lancaster KC. Asymmetry Measures for Automated Melanoma Detection in Dermoscopic Images. [Doctoral Dissertation]. University of Houston; 2018. Available from: http://hdl.handle.net/10657/3137