Maglogiannis Ilias, Delibasis Konstantinos K
Department of Digital Systems, University of Piraeus, Greece.
University of Thessaly, Department of Computer Science and Biomedical Informatics, Greece.
Comput Methods Programs Biomed. 2015 Feb;118(2):124-33. doi: 10.1016/j.cmpb.2014.12.001. Epub 2014 Dec 9.
The interest in image dermoscopy has been significantly increased recently and skin lesion images are nowadays routinely acquired for a number of skin disorders. An important finding in the assessment of a skin lesion severity is the existence of dark dots and globules, which are hard to locate and count using existing image software tools. In this work we present a novel methodology for detecting/segmenting and count dark dots and globules from dermoscopy images. Segmentation is performed using a multi-resolution approach based on inverse non-linear diffusion. Subsequently, a number of features are extracted from the segmented dots/globules and their diagnostic value in automatic classification of dermoscopy images of skin lesions into melanoma and non-malignant nevus is evaluated. The proposed algorithm is applied to a number of images with skin lesions with known histo-pathology. Results show that the proposed algorithm is very effective in automatically segmenting dark dots and globules. Furthermore, it was found that the features extracted from the segmented dots/globules can enhance the performance of classification algorithms that discriminate between malignant and benign skin lesions, when they are combined with other region-based descriptors.
近年来，人们对图像皮肤镜检查的兴趣显著增加，如今针对多种皮肤疾病会常规采集皮肤病变图像。在评估皮肤病变严重程度时的一个重要发现是存在暗点和小球体，而使用现有的图像软件工具很难定位和计数这些暗点和小球体。在这项工作中，我们提出了一种从皮肤镜图像中检测/分割并计数暗点和小球体的新方法。分割是使用基于逆非线性扩散的多分辨率方法进行的。随后，从分割出的点/小球体中提取一些特征，并评估它们在将皮肤病变的皮肤镜图像自动分类为黑色素瘤和非恶性痣方面的诊断价值。所提出的算法应用于一些具有已知组织病理学的皮肤病变图像。结果表明，所提出的算法在自动分割暗点和小球体方面非常有效。此外，还发现从分割出的点/小球体中提取的特征与其他基于区域的描述符相结合时，可以提高区分恶性和良性皮肤病变的分类算法的性能。
