Department of Computer Science, Texas A&M University-Commerce, Commerce, Texas, USA.
BMC Bioinformatics. 2010 Oct 7;11 Suppl 6(Suppl 6):S23. doi: 10.1186/1471-2105-11-S6-S23.
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.
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.
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.
皮肤镜检查是诊断黑色素瘤和其他色素性皮肤病变的主要成像方式之一。自动化评估皮肤镜图像的工具已成为一个重要的研究领域，主要是因为人类解释的观察者内和观察者间的差异。皮肤镜图像分析中最重要的步骤之一是检测病变边界，因为许多其他特征，如不对称性、边界不规则和边界突然截断，都依赖于病变的边界。
为了实现病变轮廓的自动化处理，我们在 50 张皮肤镜图像上使用主动轮廓模型（ACM）和基于边界的密度聚类（BD-DBSCAN）算法，这些图像也有ground truth 用于定量比较。我们观察到，ACM 和 BD-DBSCAN 在所有图像上的边界误差相同，均为 6.6%。为了解决噪声图像的问题，BD-DBSCAN 可以比 ACM 更好地进行轮廓描绘。然而，当使用最佳参数时，ACM 优于 BD-DBSCAN，因为 ACM 的召回率更高。
我们成功提出了两种新的框架来描绘可疑病变，分别是 i）带有锐化功能的 ACM 集成方法，ii）快速基于边界的密度聚类技术。ACM 使曲线向病变边界收缩。为了指导演化，模型采用特定形式的几何热偏微分方程的精确解 27。为了使 ACM 通过噪声图像前进，正在考虑改进模型的边界条件。BD-DBSCAN 改进了常规的基于密度的算法，以便智能地选择查询点。
