Lesion detection in demoscopy images with novel density-based and active contour approaches

BMC Bioinformatics

Proceedings

Mutlu Mete¹  Nikolay Metodiev Sirakov² 

[1] Department of Computer Science, Texas A&M University–Commerce, Commerce, Texas, USA;Department of Computer Science, Texas A&M University–Commerce, Commerce, Texas, USA;Department of Mathematics, Texas A&M University–Commerce, Commerce, Texas, USA;

关键词: Active Contour;    Markov Random Field;    Query Point;    Active Contour Model;    Neighborhood Query;   

DOI  :  10.1186/1471-2105-11-S6-S23

来源: Springer

【 摘 要 】

BackgroundDermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion.ResultsTo automate the process of delineating the lesions, we employed Active Contour Model (ACM) and boundary-driven density-based clustering (BD-DBSCAN) algorithms on 50 dermoscopy images, which also have ground truths to be used for quantitative comparison. We have observed that ACM and BD-DBSCAN have the same border error of 6.6% on all images. To address noisy images, BD-DBSCAN can perform better delineation than ACM. However, when used with optimum parameters, ACM outperforms BD-DBSCAN, since ACM has a higher recall ratio.ConclusionWe successfully proposed two new frameworks to delineate suspicious lesions with i) an ACM integrated approach with sharpening and ii) a fast boundary-driven density-based clustering technique. ACM shrinks a curve toward the boundary of the lesion. To guide the evolution, the model employs the exact solution [27] of a specific form of the Geometric Heat Partial Differential Equation [28]. To make ACM advance through noisy images, an improvement of the model’s boundary condition is under consideration. BD-DBSCAN improves regular density-based algorithm to select query points intelligently.

【 授权许可】

CC BY   
© Mete and Sirakov; licensee BioMed Central Ltd. 2010