Fusion of Color Bands Using Genetic Algorithm to Segment Melanoma

R. L. Araújo, D. Ushizima, Romuere R. V. Silva
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引用次数: 2

Abstract

Melanoma is often associated with changes in the color, size or shape of a mole. Several tools, including smartphone apps, have been developed for the detection of melanoma through medical images. To interpret the information in these images efficiently, it is necessary to isolate key regions of interest. In this research, we analyze the impact of evolutionary computing on the segmentation of melanoma through the fusion of color bands from the images. The tests performed on the PH2 image base showed a 20% improvement in the average Dice compared to using the standard intensity, a promising result toward obtaining more efficient and accurate melanoma screening.
基于遗传算法的色带融合黑色素瘤分割
黑色素瘤通常与痣的颜色、大小或形状的变化有关。包括智能手机应用程序在内的一些工具已经被开发出来,用于通过医学图像检测黑色素瘤。为了有效地解释这些图像中的信息,有必要隔离感兴趣的关键区域。在这项研究中,我们分析了进化计算对黑色素瘤的影响,通过融合图像的色带。在PH2图像基础上进行的测试显示,与使用标准强度相比,平均Dice提高了20%,这是获得更有效和准确的黑色素瘤筛查的有希望的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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