IMAGE SEGMENT FOR LUNG NODULE DETECTION USING CONSTRAINED OPTIMIZATION

恶性肺结节的自动检测可能发挥潜在的作用 对于肺癌的检测具有重要的作用。的一个重要组成部分 该检测过程是对肺结节的准确分割。由于 结节的小尺寸、密度值和存在的差异 对于周围血管，常用的图像分割方法不能 表现令人满意。我们正在开发一种使用 计算优化技术。优化问题是 配方可实现降噪、边缘清脆和生产 均匀和均匀的区域。这之后是边 检测、轮廓补全和组合形成最终的 分段。我们的初步结果表明，该方法能够 生成具有极其准确的边缘描述的分割 结节。我们打算进一步测试和加强我们的方法。

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.