中国医疗设备2017，Vol.32Issue(11)：66-70,81,6.DOI:10.3969/j.issn.1674-1633.2017.11.016
基于sigmoid边缘模型的低对比度 图像分割算法研究
Improved Segmentation of Low-Contrast Lesions Using Sigmoid Edge Model
摘要
Abstract
Objective We used a smoothed noisy intensity profile by a sigmoid function and employ it to discover the true location of CT/MR tumor boundary more accurately.Methods A novel combination of the support vector machine, watershed, and scattered data approximation algorithms were employed to initially segment a tumor. Small and large abnormalities were treated distinctly. Next, the proposed sigmoid edge model was fitted to the normal profile of the border. The estimated parameters of the model were then utilized to find true boundary of a tissue. The quantitative metrics were evaluated by liver segmentation challenge proposed by Medical Image Computing and Computer Assisted Intervention.Results We extensively evaluated our method using synthetic images (contaminated with varying levels of noise) and clinical CT/MR data. Based on the sensitivity analysis Results , we decided to set the threshold for data approximation, number of sectors anddgap as 15, 12 and 4, respectively. Visually and quantitatively experimental Results indicated that VOE and RVD of the proposed method were 28.21% and 19.20% in the first team and 7.62% and 13.45% in the second team, which were superior to the existing Methods .Conclusion For different size and any types of tumors, the proposed method can obtain more efficient and accurate segmentation Results . It can also provide better robustness, superiority, and pervasiveness in the noise environment and clinical applications.关键词
sigmoid边缘模型/图像分割/离散数据/肿瘤分割/分水岭算法Key words
sigmoid edge model/image segmentation/scattered data/tumor segmentation/watershed algorithm分类
信息技术与安全科学引用本文复制引用
丁力,周啸虎,陈宇辰,张子齐..基于sigmoid边缘模型的低对比度 图像分割算法研究[J].中国医疗设备,2017,32(11):66-70,81,6.基金项目
国家自然科学青年基金(81601477). （81601477）
