摘要
针对HIFU超声图像中子宫肌瘤的分割难题,该文提出了一种准确高效的引入局部全局信息的区域自适应局域化快速活动轮廓模型.该模型引入了图像的局部全局信息形成局部全局力,并依据演化曲线上各点周围的灰度分布均匀程度动态地决定图像的局部全局信息和形状约束信息的使用范围,克服了HIFU子宫肌瘤超声图像分割中的初始化轮廓敏感性问题.该模型通过利用该灰度分布信息自适应地改变局域区域的半径大小,进而在活动轮廓曲线演化过程中动态地调整局域化区域范围,提高了分割的准确性及曲线的演化效率.最后在演化曲线上通过使用同一局部区域计算相邻像素的局域作用力,进一步提高了分割效率,最终实现了HIFU子宫肌瘤超声图像的准确高效分割.实验结果表明:该文方法克服了HIFU子宫肌瘤超声图像分割的难题,相较于最近提出的MSLCV模型,获得了更准确高效的分割结果,且平均计算效率提高了84.6%.
To solve the problems in segmenting HIFU （High Intensity Focused Ultrasound） ultrasound image of uterine fibroids,we propose an adaptive localized region-based fast active contour model by introducing HIFU image’s global information in local region,which is more accurate as well as more efficient.The proposed segmenting model incorporates HIFU ultrasound image’s global information in local region to form a locally global force.Meanwhile the gray level distribution uniformity around each pixel point on the evolution curve is calculated to dynamically determine the various application condition of HIFU image’s global information in local region and the shape constrained information of the uterine fibroids in HIFU images,which is assigned to overcome the sensitivity of the initialized contour by applying the locally global force when segmenting HIFU ultrasound image of uterine fibroids.By using the calculated gray level distribution uniformity around each pixel point on the evolution curve,the adaptive localized region-based fastnbsp;active contour model adaptively changes the local radius of the localized region,and then dynamically adjusts the size of localized region during the evolution process of the active contour curve, achieving more accurate and more efficient segmentation results of HIFU ultrasound image of uterine fibroids.By applying the same localized region to calculate the local forces of adjacent pixel points on the evolution curve,our method further improves the segmentation efficiency, finally achieving accurate and efficient segmentation of HIFU ultrasound image of uterine fibroids. The experimental results show that compared with recently proposed MSLCV （Multi-scale and Shape Constrained Localized C-V）model,our method solves the problems in segmenting HIFU ultrasound image of uterine fibroids as well as improves the segmentation accuracy and increases the average segmentation efficient by 84.6%.
出处
《计算机学报》
EI
CSCD
北大核心
2016年第7期1464-1476,共13页
Chinese Journal of Computers
基金
国家自然科学基金(61373107)
国家"九七三"重点基础研究发展规划项目基金(2011CB707904)
北航虚拟现实技术与系统国家重点实验室开放课题基金(BUAA-VR-13KF-15)资助
关键词
HIFU子宫肌瘤超声图像分割
活动轮廓模型
局部全局信息
自适应局域化
MSLCV模型
水平集
segmentation of HIFU uterine fibroids ultrasound images
active contour model
global information in local region
adaptive localized region
MSLCV model
level set
