Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system
Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system
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DOI：
10.1097/01.cmr.0000215041.76553.58
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发表时间：
2006-04-01
影响因子：
2.2
通讯作者：
Tanaka, M
中科院分区：
文献类型：
--
作者：
Iyatomi, H;Oka, H;Tanaka, M
本研究的目的是提供一个定量评估皮肤科医生提取的肿瘤面积，并评估基于计算机的方法，从皮肤镜图像完善基于计算机的黑色素瘤诊断系统。对188例Clark痣、56例Reed痣和75例黑色素瘤进行了皮肤镜检查。五位皮肤科医生用平板电脑手动绘制每个病灶的边界。评价观察者间的变异性，并定义每个皮肤镜图像的标准肿瘤面积（STA）。手动提取10个非医疗个人和两个基于计算机的方法进行了评估与STA为基础的评估标准：精度和召回。我们的新的基于计算机的方法引入了区域增长的方法，以产生接近皮肤科医生获得的结果。我们的提取方法的有效性方面的诊断准确性进行了评估。两个线性分类器建立了传统的和新的基于计算机的肿瘤区域提取方法的结果。通过绘制每个分类器的受试者工作曲线（ROC）来评估最终诊断准确性，并评估每个ROC下的面积。由5名皮肤科医生和10名非医学人员提取的肿瘤面积的标准差分别为8.9%和10.7%。在皮肤科医生对提取结果进行评估后，STA被定义为由两名以上皮肤科医生选择的区域。皮肤科医生选择的黑色素瘤区域与Clark痣或Reed痣的差异有统计学意义（P = 0.05）。相比之下，非医疗人员没有表现出这种差异。我们的新的基于计算机的提取算法表现出上级的性能（精度，94.1%，召回率，95.3%），传统的阈值方法（精度，99.5%，召回率，876%）。这些结果表明，我们的新算法提取的肿瘤区域接近皮肤科医生获得的肿瘤区域，特别是肿瘤的边界部分被充分提取。通过这种改进，ROC下的面积从0.795增加到0.875，当灵敏度为80%时，诊断准确性显示特异性增加约20%。可以得出结论，我们的基于计算机的肿瘤提取算法提取了与皮肤科医生获得的几乎相同的区域，并提高了基于计算机的诊断准确性。
The aims of this study were to provide a quantitative assessment of the tumour area extracted by dermatologists and to evaluate computer-based methods from dermoscopy images for refining a computer-based melanoma diagnostic system. Dermoscopic images of 188 Clark naevi, 56 Reed naevi and 75 melanomas were examined. Five dermatologists manually drew the border of each lesion with a tablet computer. The inter-observer variability was evaluated and the standard tumour area (STA) for each dermoscopy image was defined. Manual extractions by 10 non-medical individuals and by two computer-based methods were evaluated with STA-based assessment criteria: precision and recall. Our new computer-based method introduced the region-growing approach in order to yield results close to those obtained by dermatologists. The effectiveness of our extraction method with regard to diagnostic accuracy was evaluated. Two linear classifiers were built using the results of conventional and new computer-based tumour area extraction methods. The final diagnostic accuracy was evaluated by drawing the receiver operating curve (ROC) of each classifier, and the area under each ROC was evaluated. The standard deviations of the tumour area extracted by five dermatologists and 10 non-medical individuals were 8.9% and 10.7%, respectively. After assessment of the extraction results by dermatologists, the STA was defined as the area that was selected by more than two dermatologists. Dermatologists selected the melanoma area with statistically smaller divergence than that of Clark naevus or Reed naevus (P = 0.05). By contrast, non-medical individuals did not show this difference. Our new computer-based extraction algorithm showed superior performance (precision, 94.1%; recall, 95.3%) to the conventional thresholding method (precision, 99.5%; recall, 876%). These results indicate that our new algorithm extracted a tumour area close to that obtained by dermatologists and, in particular, the border part of the tumour was adequately extracted. With this refinement, the area under the ROC increased from 0.795 to 0.875 and the diagnostic accuracy showed an increase of approximately 20% in specificity when the sensitivity was 80%. It can be concluded that our computer-based tumour extraction algorithm extracted almost the same area as that obtained by dermatologists and provided improved computer-based diagnostic accuracy.