title: Oriented pattern analysis for streak detection in dermoscopy images.
abstract: There is an increasing demand for automated detection and analysis of dermoscopy structures and malignancy clues such as streaks in dermoscopy images, for computer-aided early diagnosis of deadly melanoma. This paper presents a novel approach for streak detection and visualization on dermoscopic images. We tackle the detection of streaks by means of ridge and valley estimation. Orientation estimation and correction is applied to detect low contrast and fuzzy streaks lines, and candidate streaks are used to classify dermoscopy images into streaks Absent or Present with the AUC of 90.5% on 300 dermoscopy images. Our approach can also detect starburst pattern of regular streaks using detected linear structures with accuracy of 81.5% and AUC of 87.7%.
authors.affiliation: School of Computing Science, Simon Fraser University, Canada. msa68@sfu.ca
authors.forename: Maryam
authors.initials: M
authors.lastname: Sadeghi
authors.forename: Tim K
authors.initials: TK
authors.lastname: Lee
authors.forename: David
authors.initials: D
authors.lastname: McLean
authors.forename: Harvey
authors.initials: H
authors.lastname: Lui
authors.forename: M Stella
authors.initials: MS
authors.lastname: Atkins
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001331:Automation; D046169:Dermoscopy / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003952:Diagnostic Imaging / Q000379:methods; D055088:Early Detection of Cancer; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008545:Melanoma / Q000175:diagnosis* / Q000473:pathology; D008962:Models, Theoretical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012984:Software