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Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold

机译:使用显着性与OTSU阈值相结合的Dermoscopy图像自动分割

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摘要

Segmentation is one of the crucial steps for the computer-aided diagnosis (CAD) of skin cancer with dermoscopy images. To accurately extract lesion borders from dermoscopy images, a novel automatic segmentation algorithm using saliency combined with Otsu threshold is proposed in this paper, which includes enhancement and segmentation stages. In the enhancement stage, prior information on healthy skin is extracted, and the color saliency map and brightness saliency map are constructed respectively. By fusing the two saliency maps, the final enhanced image is obtained. In the segmentation stage, according to the histogram distribution of the enhanced image, an optimization function is designed to adjust the traditional Otsu threshold method to obtain more accurate lesion borders. The proposed model is validated from enhancement effectiveness and segmentation accuracy. Experimental results demonstrate that our method is robust and performs better than other state-of-the-art methods.
机译:分割是皮肤癌与Dermoscopy图像的计算机辅助诊断(CAD)的关键步骤之一。 为了精确提取来自Dermoscopy图像的病变边框,本文提出了一种使用显着性与OTSU阈值结合的新型自动分割算法,包括增强和分段阶段。 在增强阶段,提取有关健康皮肤的先前信息,分别构建色调图和亮度显着性图。 通过熔断两个显着性图,获得最终增强图像。 在分段阶段,根据增强图像的直方图分布,设计优化功能以调整传统的OTSU阈值方法以获得更准确的病变边框。 提出的模型从增强效果和分割准确度验证。 实验结果表明,我们的方法是坚固的,而且比其他最先进的方法更好。

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