Optical Flow Based Background Subtraction Method for Lung Nodule Segmentation

Suji, R. Jenkin; Bhadauria, Sarita Singh; Dhar, Joydip; Godfrey, W. Wilfred

doi:10.1007/978-981-15-4015-8_23

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Researchers have used various techniques for lung nodule detection and segmentation such as 3D tensor filtering with local image feature analysis [18], global optimal active contour model [54], corner seeded region growing combined with differential evolution based optimal thresholding [35], connected component labelling with morphological operations and multilayer perceptron [21], sparse field level sets and boosting algorithm [39], adaptive ROI with multi-view residual learning [49], LBF active contour model with information entropy and joint vector [22] etc. including optical flow methods for evaluation of interval change in metastatic lung nodules [17], and segmentation by background subtraction [45]. A review of various lung nodule detection methods used by researchers can be found in [42,52,53] Chromanska and Macura [10] have listed some of the imaging features of benign and malignant nodules/lesions.…”

Section: Challenges To Lung Cancer Detectionmentioning

confidence: 99%

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

et al. 2020

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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.

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