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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Network patterns recognition for automatic dermatologic images classification
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Network patterns recognition for automatic dermatologic images classification

机译：网络模式识别，用于自动皮肤图像分类

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摘要

In this paper we focus on the problem of automatic classification of melanocytic lesions, aiming at identifying the presence of reticular patterns. The recognition of reticular lesions is an important step in the description of the pigmented network, in order to obtain meaningful diagnostic information. Parameters like color, size or symmetry could benefit from the knowledge of having a reticular or non-reticular lesion. The detection of network patterns is performed with a three-steps procedure. The first step is the localization of line points, by means of the line points detection algorithm, firstly described by Steger. The second step is the linking of such points into a line considering the direction of the line at its endpoints and the number of line points connected to these. Finally a third step discards the meshes which couldn't be closed at the end of the linking procedure and the ones characterized by anomalous values of area or circularity. The number of the valid meshes left and their area with respect to the whole area of the lesion are the inputs of a discriminant function which classifies the lesions into reticular and non-reticular. This approach was tested on two balanced (both sets are formed by 50 reticular and 50 non-reticular images) training and testing sets. We obtained above 86% correct classification of the reticular and non-reticular lesions on real skin images, with a specificity value never lower than 92%.
机译：在本文中，我们着眼于黑素细胞病变的自动分类问题，旨在确定网状结构的存在。为了获得有意义的诊断信息，网状病变的识别是色素网络描述中的重要步骤。颜色，大小或对称性等参数可受益于具有网状或非网状病变的知识。网络模式的检测通过三步过程执行。第一步是通过首先由Steger描述的线点检测算法来定位线点。第二步是考虑这些线在其端点处的方向以及连接到这些端点的线点的数量，将这些点链接到一条线中。最后，第三步丢弃在链接过程结束时无法关闭的网格以及以面积或圆度异常值为特征的网格。剩余的有效网格数及其相对于病变整个区域的面积是判别函数的输入，该函数将病变分为网状和非网状。该方法在两个平衡的训练和测试集上进行了测试（两组均由50个网状图像和50个非网状图像组成）。我们在真实皮肤图像上获得了86％以上的网状和非网状病变的正确分类，特异性值始终不低于92％。

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