ABV-IIITM Gwalior, ABV-IIITM Campus, Morena Link Road, Gwalior, MadhyaPradesh, 474010, India.
RGPV Bhopal, Gandhi Nagar, MadhyaPradesh, 462033, India.
J Digit Imaging. 2020 Oct;33(5):1306-1324. doi: 10.1007/s10278-020-00346-w.
Lung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology and intensity information to perform segmentation of regions of interest in videos. CT scans comprise of a sequence of dicom 2-D image slices similar to videos which also comprise of a sequence of image frames ordered on a timeline. In this work, Farneback, Horn-Schunck and Lucas-Kanade optical flow methods have been used for processing the dicom slices. The novelty of this work lies in the usage of optical flow methods, generally used in motion-based segmentation tasks, for the segmentation of nodules from CT images. Since thin-sliced CT scans are the imaging modality considered, they closely approximate the motion videos and are the primary motivation for using optical flow for lung nodule segmentation. This paper also provides a detailed comparative analysis and validates the effectiveness of using optical flow methods for segmentation. Finally, we propose methods to further improve the efficiency of segmentation using optical flow methods on CT scans.
肺结节分割是肺癌检测和诊断中任何 CAD 系统的基本步骤。传统的图像分割方法主要基于形态或强度。基于运动的分割技术倾向于使用时间信息以及形态和强度信息来对视频中的感兴趣区域进行分割。CT 扫描由类似于视频的一系列 dicom 2-D 图像切片组成，视频也由按时间线排序的一系列图像帧组成。在这项工作中，Farneback、Horn-Schunck 和 Lucas-Kanade 光流方法已用于处理 dicom 切片。这项工作的新颖之处在于将光流方法（通常用于基于运动的分割任务）用于从 CT 图像中分割结节。由于考虑使用薄切片 CT 扫描作为成像方式，它们非常接近运动视频，这是使用光流进行肺结节分割的主要动机。本文还提供了详细的比较分析，并验证了在 CT 扫描上使用光流方法进行分割的有效性。最后，我们提出了使用光流方法进一步提高 CT 扫描上分割效率的方法。
