Meskini E, Helfroush M S, Kazemi K, Sepaskhah M
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.
Molecular Dermatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
J Biomed Phys Eng. 2018 Mar 1;8(1):117-126. eCollection 2018 Mar.
With advances in medical imaging systems, digital dermoscopy has become one of the major imaging modalities in the analysis of skin lesions. Thus, automated segmentation or border detection has a great impact on the subsequent steps of skin cancer computer-aided diagnosis using demoscopy images. Since dermoscopy images suffer from artifacts such as shading and hair, there is a need for automated and robust artifact attenuation removal and lesion border detection.
method for segmentation of dermoscopy images is proposed based on active contour. To this end, at first, a simple method for hair pixels is restored and a new scheme for shading detection is proposed. Then, particle swarm optimization (PSO) algorithm is applied to select the best coefficients for converting RGB to gray level. The obtained gray level image is then used as input for multi Otsu method which provides initial contour for border detection using active contour. Finally, Chan and Vese active contour is used for final lesion border detection.
The method is tested on a total of 145 dermoscopic images: 79 cases with benign lesion and 75 cases with melanoma lesion. Mean accuracy, sensitivity and specificity were obtained 94%, 78.5% and 99%, respectively.
Results reveal that the proposed method segments the lesion from dermoscopy images accurately.
随着医学成像系统的发展，数字皮肤镜检查已成为分析皮肤病变的主要成像方式之一。因此，自动分割或边界检测对使用皮肤镜图像的皮肤癌计算机辅助诊断的后续步骤有很大影响。由于皮肤镜图像存在诸如阴影和毛发等伪影，因此需要自动且强大的伪影衰减去除和病变边界检测。
提出了一种基于活动轮廓的皮肤镜图像分割方法。为此，首先，恢复了一种简单的毛发像素处理方法，并提出了一种新的阴影检测方案。然后，应用粒子群优化（PSO）算法选择将RGB转换为灰度级的最佳系数。将获得的灰度图像用作多Otsu方法的输入，该方法为使用活动轮廓进行边界检测提供初始轮廓。最后，使用Chan和Vese活动轮廓进行最终的病变边界检测。
该方法在总共145张皮肤镜图像上进行了测试：79例良性病变和75例黑色素瘤病变。平均准确率、灵敏度和特异性分别为94%、78.5%和99%。
结果表明，所提出的方法能够准确地从皮肤镜图像中分割病变。
