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. 2020 Oct;33(5):1306-1324.
doi: 10.1007/s10278-020-00346-w.

Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images

R Jenkin Suji  1 , Sarita Singh Bhadouria  2 , Joydip Dhar  3 , W Wilfred Godfrey  3
Affiliations

Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images

R Jenkin Suji et al. J Digit Imaging. 2020 Oct.

Abstract

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.

Keywords: Computed tomography; Optical flow; Pulmonary nodule; Segmentation.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Proposed methodology
Fig. 2
Fig. 2
Reference slice of LIDC-IDRI-0001, slice number 85
Fig. 3
Fig. 3
Current slice of LIDC-IDRI-0001, slice number 86
Fig. 4
Fig. 4
Visualization of the flow vectors of LIDC-IDRI-0001, obtained by applying the three discussed flow methods, namely Farneback, Horn-Schunk and dense Lucas-Kanade respectively with respect to slice numbers 86–91 obtained in reference to the reference slice 85 (mode 0) along with the nodule contour marked (in red) by the radiologist
Fig. 5
Fig. 5
Visualization of the flow vectors of LIDC-IDRI-0001, obtained by applying the three discussed flow methods, namely Farneback, Horn-Schunk and dense Lucas-Kanade respectively, with respect to thresholded slice numbers 86–91 obtained in reference to the reference slice 85 (mode 0) along with the nodule contour marked (in red) by the radiologist
Fig. 6
Fig. 6
Slice sequences of LIDC-IDRI-0001, slice numbers 86–87 along with the nodule contour (marked in red) as marked by the radiologist
Fig. 7
Fig. 7
Segmented Mask obtained by applying optical flow (Horn-Schunck)
Fig. 8
Fig. 8
LIDC-IDRI-0001, slice number 86—After applying parenchyma segmentation
See this image and copyright information in PMC

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