Ramezani Maryam, Karimian Alireza, Moallem Payman
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
J Med Signals Sens. 2014 Oct;4(4):281-90.
In order to distinguish between benign and malignant types of pigmented skin lesions, computerized procedures have been developed for images taken by different equipment that the most available one of them is conventional digital cameras. In this research, a new procedure to detect malignant melanoma from benign pigmented lesions using macroscopic images is presented. The images are taken by conventional digital cameras with spatial resolution higher than one megapixel and by considering no constraints and special conditions during imaging. In the proposed procedure, new methods to weaken the effect of nonuniform illumination, correction of the effect of thick hairs and large glows on the lesion and also, a new threshold-based segmentation algorithm are presented. 187 features representing asymmetry, border irregularity, color variation, diameter and texture are extracted from the lesion area and after reducing the number of features using principal component analysis (PCA), lesions are determined as malignant or benign using support vector machine classifier. According to the dermatologist diagnosis, the proposed processing methods have the ability to detect lesions area with high accuracy. The evaluation measures of classification have indicated that 13 features extracted by PCA method lead to better results than all of the extracted features. These results led to an accuracy of 82.2%, sensitivity of 77% and specificity of 86.93%. The proposed method may help dermatologists to detect the malignant lesions in the primary stages due to the minimum constraints during imaging, the ease of usage by the public and nonexperts, and high accuracy in detection of the lesion type.
为了区分色素沉着性皮肤病变的良性和恶性类型，已开发出针对不同设备拍摄图像的计算机化程序，其中最常用的设备是传统数码相机。在本研究中，提出了一种利用宏观图像从良性色素沉着病变中检测恶性黑色素瘤的新程序。这些图像由空间分辨率高于100万像素的传统数码相机拍摄，并且在成像过程中不考虑任何限制和特殊条件。在所提出的程序中，提出了减弱非均匀光照影响的新方法、校正病变上浓密毛发和大光斑影响的方法，以及一种基于新阈值的分割算法。从病变区域提取代表不对称性、边界不规则性、颜色变化、直径和纹理的187个特征，在使用主成分分析（PCA）减少特征数量后，使用支持向量机分类器将病变确定为恶性或良性。根据皮肤科医生的诊断，所提出的处理方法能够高精度地检测病变区域。分类评估指标表明，通过PCA方法提取的13个特征比所有提取的特征产生更好的结果。这些结果导致准确率为82.2%，灵敏度为77%，特异性为86.93%。所提出的方法可能有助于皮肤科医生在早期阶段检测恶性病变，这是因为成像过程中的限制最小、公众和非专业人员易于使用，并且在病变类型检测方面具有高精度。
