IMAGE SEGMENT FOR LUNG NODULE DETECTION USING CONSTRAINED OPTIMIZATION

使用约束优化进行肺结节检测的图像片段

• 批准号: 6411729
• 负责人: THOMAS F COLEMAN
• 金额: $ 1.29万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-12-01 至 2001-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6411729
• 关键词: bioimaging /biomedical imaging; biomedical resource; computed axial tomography; computers; technology /technique

Automatic detection of malignant lung nodule can potentially play an important role for lung cancer detection. A crucial component of this detection process is accurate lung nodule segmentation. Due to the small size of the nodule, variance in density values and presence of peripheral blood vessels, common image segmentation methods fail to perform satisfactorily. We are developing an image segmentation using computational optimization techniques. The optimization problem is formulated to achieve noise reduction, edge crispening and production of homogeneous and uniform regions. This is followed by edge detection, contour completion and combination to form the final segmentation. Our preliminary results suggest that the method is able to produce segmentation with a faily accurate edge description of the nodule. We intend to further test and strengthen our method.

恶性肺结节的自动检测可以潜在地发挥 对肺癌检测有重要作用。 的关键组成部分 该检测过程是准确的肺结节分割。 由于 结节的小尺寸、密度值的变化和存在 对于外周血管，常见的图像分割方法不能 表现令人满意。 我们正在开发一个图像分割使用 计算优化技术。 优化问题 制定，以实现降噪，边缘卷曲和生产 均匀和统一的区域。 其次是edge 检测、轮廓补全和组合，形成最终的 细分 我们的初步结果表明，该方法是能够 产生分割与faily准确的边缘描述的 结节。 我们打算进一步测试和加强我们的方法。