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A novel fusion approach for segmenting dermoscopy image based on region consistency

机译:基于区域一致性分割皮肤镜图像的融合方法

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摘要

Malignant melanoma is among the most rapidly increasing cancers in the world. Image border detection is often the first step to characterize skin lesion for the follow-up computer-aided diagnosis. Existing approaches lack robustness in the face of dermoscopy images varying in size, color, texture, and structure. In this paper, a novel approach is proposed to fuse the segmentation results obtained from different algorithms either in the gray-scale or color space, by discarding the subregions similar to the background skin based on their region consistencies in intensity, size, and texture. The experimental results on the real dermoscopy image set demonstrate that the proposed method can improve the overall performance in terms of both accuracy and robustness.
机译:恶性黑色素瘤是世界上增长最快的癌症之一。图像边界检测通常是表征皮肤病变的第一步,用于后续的计算机辅助诊断。现有方法在面对尺寸,颜色,纹理和结构变化的皮肤镜图像时缺乏鲁棒性。在本文中,提出了一种新颖的方法,通过基于强度,大小和纹理的区域一致性丢弃与背景皮肤相似的子区域,融合从灰度或色彩空间中的不同算法获得的分割结果。在真实皮肤镜图像集上的实验结果表明,该方法可以在准确性和鲁棒性方面提高整体性能。

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