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


RESULTS
To 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.


CONCLUSIONS
We 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.
Mete
Mutlu
M
Department of Computer Science, Texas A&M University-Commerce, Commerce, Texas, USA. Mutlu_Mete@tamu-commerce.edu
Sirakov
Nikolay Metodiev
NM
D046169:Dermoscopy / Q000295:instrumentation* / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000175:diagnosis* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000473:pathology