Automated melanoma recognition
Automated melanoma recognition
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DOI：
10.1109/42.918473
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发表时间：
2001-03-01
影响因子：
10.6
通讯作者：
Kittler, H
中科院分区：
文献类型：
--
作者：
Ganster, H;Pinz, A;Kittler, H
为了提高对恶性黑色素瘤的早期识别，开发了一种计算机化的ELM图像分析系统。作为第一步，皮肤病变的二值掩码由几种基本分割算法和融合策略确定，计算一组包含形状和辐射特征以及局部和全局参数的特征来描述病变的恶性程度。然后应用统计特征子集选择方法从中选择重要特征。最终的kNN分类灵敏度为87%，特异性为92%。
A system for the computerized analysis of images obtained from ELM has been developed to enhance the early recognition of malignant melanoma, As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy, A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion, Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%.