Application of simulated annealing to biosignal classification and segmentation

B. Cigale, M. Divjak, D. Zazula
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引用次数: 5

Abstract

Simulated annealing is no doubt one of the most popular optimisation methods. Like similar methods it is based on natural phenomena. In the paper two examples of how to use this method in medical applications are presented. In the first example the successfulness of simulated annealing is compared to a genetic algorithm on the problem of the optimal coefficients determination for cellular neural networks, when used for segmentation of ovarian ultrasound images. In this field the latter method produced better results. The second example discusses the usage of simulated annealing for optimisation of signal classification.
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模拟退火在生物信号分类和分割中的应用
模拟退火无疑是最流行的优化方法之一。和其他方法一样,它是基于自然现象的。本文给出了该方法在医学应用中的两个实例。在第一个例子中,当用于卵巢超声图像分割时,模拟退火与遗传算法在细胞神经网络最优系数确定问题上的成功进行了比较。在这个领域,后一种方法产生了更好的结果。第二个例子讨论了模拟退火对信号分类优化的使用。
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