Large lung tumor segmentation in CT images based on random walk algorithm

doi:10.3969/j.issn.1000-1565.2019.03.013

Abstract

Abstract: To the segmentation accuracy and adaption of large lung tumors with complex structures in CT images, a segmentation method based on random walk algorithm is proposed. First, the lung parenchyma was extracted according to the gray level information. If the large tumors adhered to normal tissues surrounding lungs, the lung parenchymas with concave edges are first extracted, and then the lung parenchyma with concave edges is refined by the curve section deformation model according to the prior shape of lung. Then a random walk algorithm is implemented, where the object and background seeds are automatically found by region growing method,and interaction are needed to modify a few background seed points if vasa adhere to tumors. The experiment results show that this method is accurate, which can meet the requirements of clinical treatment and pathological analysis.

Key words: lung tumor segmentation, random walk algorithm, curve section deformation model

CLC Number:

TP391

ZHANG Xin, WANG Jie, KONG Hang, WEN Yutong, LIU Wen, WANG Bing. Large lung tumor segmentation in CT images based on random walk algorithm[J]. Journal of Hebei University (Natural Science Edition), 2019, 39(3): 311-315.

References

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