Department of Computer Science, Louisiana State University, Shreveport, LA, USA.
Skin Res Technol. 2013 Feb;19(1):e252-8. doi: 10.1111/j.1600-0846.2012.00636.x. Epub 2012 Jun 7.
Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice.
In this article, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thres holding methods.
Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state-of-the-art methods.
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。由于人类解释的难度和主观性，皮肤镜图像的自动分析已成为一个重要的研究领域。边界检测通常是分析的第一步。在许多情况下，可以使用应用于蓝色通道的阈值方法将病变大致与背景皮肤分开。然而，似乎没有一种单一的阈值方法能够足够稳健地成功处理临床实践中遇到的各种皮肤镜图像。
本文提出了一种使用阈值方法集自动检测皮肤镜图像中病变边界的方法。
与九种最先进的方法相比，该方法在一组 90 张困难图像上的实验结果表明，该方法具有鲁棒性、快速性和准确性。
