基于局部灰度差异的快速自适应活动轮廓模型分割腮腺导管  

A fast adaptive active contour model based on local gray difference for parotid duct image segmentation

作　　者：邓旋 兰天俊 张明慧[1] 陈之锋[3] 陶谦[2] 卢振泰[1] DENG Xuan;LAN Tianjun;ZHANG Minghui;CHEN Zhifeng;TAO Qian;LU Zhentai(Key Lab for Medical Imaging of Southern Medical University, Guangzhou 510515, China;Department of Stomatology, Nanfang Hospital,Southern Medical University, Guangzhou 510515, China;Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology,Hospital of Stomatology Affiliated to Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou 510055,China)

机构地区：[1]南方医科大学医学图像处理重点实验室,广东广州510515 [2]中山大学光华口腔医学院.附属口腔医院口腔颌面头颈肿瘤外科//广东省口腔医学重点实验室,广东广州510055 [3]南方医科大学南方医院口腔科,广东广州510515

出　　处：《南方医科大学学报》2018年第12期1485-1491,共7页Journal of Southern Medical University

基　　金：广东省自然科学基金(2016A030313574;2017A030313891).

摘　　要：目的研究一种基于局部灰度差异的快速自适应活动轮廓模型腮腺导管图像分割算法。方法本研究在LBF模型的基础上,加入了轮廓内外局部灰度的均值差异作为驱动演化曲线的能量项,并且将局部灰度方差差异代替λ1、λ2作为能量参数值的控制项,同时还引入两种不同邻域大小的局部相似因子,来克服图像灰度不均匀和边界模糊的影响以提高分割效率。结果该算法在分割图像时,能够根据内外局部灰度均值差异和方差差异自适应地调节演化方向、速度以及内外部区域能量所占权重,在面对复杂梯度边界区域时亦能够检测出真实边界,使演化曲线快速精确地逼近目标边界。结论实验结果表明,本文算法明显优于现有的几种分割算法,能够实现快速精确地分割腮腺导管图像并且保留图像细节。Objective To establish a fast adaptive active contour model based on local gray difference for parotid duct image segmentation. Methods On the basis of the LBF model, we added the mean difference of the local gray scale inside and outside the contour as the energy term of the driving evolution curve, and the local gray-scale variance difference was used to replace λ1 and λ2 as the control term of the energy parameter value. Two local similarity factors of different neighborhood sizes were introduced to correct the effects of image gray unevenness and boundary blur to improve the segmentation efficiency. Results During image segmentation, this algorithm allowed for adaptive adjustment of the evolution direction, velocity and the energy weight of the internal and external regions according to the difference of gray mean and variance between the internal and external regions. This algorithm was also capable of detecting the actual boundary in a complex gradient boundary region,thus enabling the evolution curve to approach the target boundary quickly and accurately. Conclusion The proposed algorithm is superior to the existing segmentation algorithms and allows fast and accurate segmentation of the parotid duct with well-preserved image details.

关 键 词：局部灰度差异 快速自适应 局部相似因子 活动轮廓模型 腮腺导管 图像分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]