IEEE Trans Med Imaging. 2014 May;33(5):1137-47. doi: 10.1109/TMI.2014.2305769.
In this paper different model-based methods of classification of global patterns in dermoscopic images are proposed. Global patterns identification is included in the pattern analysis framework, the melanoma diagnosis method most used among dermatologists. The modeling is performed in two senses: first a dermoscopic image is modeled by a finite symmetric conditional Markov model applied to L∗a∗b∗ color space and the estimated parameters of this model are treated as features. In turn, the distribution of these features are supposed that follow different models along a lesion: a Gaussian model, a Gaussian mixture model, and a bag-of-features histogram model. For each case, the classification is carried out by an image retrieval approach with different distance metrics. The main objective is to classify a whole pigmented lesion into three possible patterns: globular, homogeneous, and reticular. An extensive evaluation of the performance of each method has been carried out on an image database extracted from a public Atlas of Dermoscopy. The best classification success rate is achieved by the Gaussian mixture model-based method with a 78.44% success rate in average. In a further evaluation the multicomponent pattern is analyzed obtaining a 72.91% success rate.
本文提出了几种基于模型的方法，用于对皮肤镜图像中的全局模式进行分类。全局模式识别包含在皮肤科医生最常使用的模式分析框架中，用于黑色素瘤诊断。建模分为两种情况：首先，将皮肤镜图像建模为应用于 L∗a∗b∗颜色空间的有限对称条件马尔可夫模型，然后将该模型的估计参数作为特征进行处理。反过来，假设这些特征的分布遵循病变的不同模型：高斯模型、高斯混合模型和特征袋直方图模型。对于每种情况，都通过使用不同距离度量的图像检索方法进行分类。主要目标是将整个色素病变分类为三种可能的模式：球形、均匀和网状。在从公共皮肤镜图谱中提取的图像数据库上，对每种方法的性能进行了广泛评估。基于高斯混合模型的方法的分类成功率最高，平均成功率为 78.44%。在进一步的评估中，对多分量模式进行了分析，得到了 72.91%的成功率。
