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: 6309550
• 负责人: THOMAS F COLEMAN
• 金额: $ 2.43万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-12-01 至 2000-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6309550
• 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.

自动检测恶性肺结节的潜力 对肺癌检测具有重要作用。 的一个重要组成部分 这个检测过程就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，常用的图像分割方法无法 表现令人满意。 我们正在开发一种图像分割方法 计算优化技术。 优化问题是 旨在实现降噪、边缘锐化和生产 同质且均匀的区域。 接下来是边缘 检测、轮廓补全并组合形成最终的 分割。 我们的初步结果表明该方法能够 产生具有不准确的边缘描述的分割 结核。 我们打算进一步测试和加强我们的方法。