Abbas Q, García I F, Rashid M
Department of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, PR China.
Br J Biomed Sci. 2010;67(4):177-83. doi: 10.1080/09674845.2010.11730316.
Malignant melanoma and basal cell carcinoma are common skin tumours. For skin lesion classification it is necessary to determine and calculate different attributes such as exact location, size, shape and appearance. It has been noted that illumination, dermoscopic gel and features such as blood vessels, hair and skin lines can affect border detection. Thus, there is a need for approaches that minimise the effect of such features. This study aims to detect multiple borders from dermoscopy with increased sensitivity and specificity for the detection of early melanoma and other pigment lesions. An automated border detection method based on minimising geodesic active contour energy and incorporating homomorphic, median and anisotropic diffusion (AD) filtering, as well as top-hat watershed transformation is used. Extensive experiments on various skin lesions were conducted on real dermoscopic images and proved to enhance accurate border detection and improve the segmentation result by reducing the error rate from 12.42% to 7.23%. The results have validated the integrated enhancement of numerous lesion border detections with the noise removal algorithm which may contribute to skin cancer classification.
恶性黑色素瘤和基底细胞癌是常见的皮肤肿瘤。对于皮肤病变分类，有必要确定并计算不同的属性，如确切位置、大小、形状和外观。已经注意到，光照、皮肤镜凝胶以及血管、毛发和皮肤纹理等特征会影响边界检测。因此，需要采用能够将此类特征的影响降至最低的方法。本研究旨在通过提高对早期黑色素瘤和其他色素性病变检测的灵敏度和特异性，从皮肤镜检查中检测多个边界。使用了一种基于最小化测地线活动轮廓能量并结合同态、中值和各向异性扩散（AD）滤波以及顶帽分水岭变换的自动边界检测方法。在真实的皮肤镜图像上对各种皮肤病变进行了广泛实验，结果表明该方法通过将错误率从12.42%降低到7.23%，增强了准确的边界检测并改善了分割结果。这些结果验证了通过噪声去除算法对众多病变边界检测的综合增强，这可能有助于皮肤癌的分类。
