基于分数阶麻雀搜索优化OTSU肺组织分割算法

Lung Tissue Segmentation Algorithm:Fractional Order Sparrow Search Optimization for OTSU

针对传统粒子群算法应用于肺组织分割时寻优慢且易陷入局部最优的问题,提出了一种基于分数阶麻雀搜索优化的最大类间差法(OTSU)肺组织分割算法。采用分数阶微积分算法优化麻雀搜索算法,根据麻雀的位置信息,引入自适应分数阶阶次以自适应地调整分数阶阶次,加快算法收敛速度;采用灰度级-梯度二维直方图以减小二维直方图的计算量和麻雀的搜索范围;算法实现过程中,利用孔洞填充算法去除CT图像背景,采用形态学操作去除噪音并修补病变区域产生的孔洞。实验表明,所提算法达到稳定的收敛次数相较于粒子群优化OTSU算法、分数阶粒子群优化OTSU算法、麻雀搜索优化OTSU算法分别减少了22.75%,13.75%,2.25%,因此所提算法在保证分割精度的同时,提高了算法的收敛速度。

Aiming at the characteristics of slow and easy to get into local optimum for traditional particle swarm optimization used for lung tissue segmentation,a lung tissue segmentation algorithm based on fractional sparrow search optimization for OTSU is proposed.Using fractional calculus algorithm to optimize sparrow search algorithm,according to the position information of sparrow,the adaptive fractional order is introduced to adjust the fractional order adaptively and accelerate the convergence speed of the algorithm.The grayscale-gradient 2 D histogram is used to reduce the computation of 2 D histogram and the search range of sparrow.During the implementation of the algorithm,the hole filling algorithm is used to remove the CT image background,and morphological operation is used to remove the noise and repair the holes in the lesion area.The experiment show that the number of stable convergence times achieved by the proposed algorithm is 22.75%,13.75%and 2.25%lower than that of particle swarm optimization OTSU algorithm,fractal-order particle swarm optimization OTSU algorithm and sparrow search optimization OTSU algorithm,respectively.Therefore,the algorithm in this paper not only guarantees the segmentation accuracy,but also improves the convergence speed of the algorithm.