A new boundary correction method for lung parenchyma

Junfang Liang; Huiqin Jiang; Ling Ma; Yumin Liu; Nakaguchi Toshiya

doi:10.1117/12.2266086

8 February 2017 A new boundary correction method for lung parenchyma

Junfang Liang, Huiqin Jiang, Ling Ma, Yumin Liu, Nakaguchi Toshiya

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022529 (2017) https://doi.org/10.1117/12.2266086
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

In order to repair the boundary depressions caused by juxtapleural nodules and improve the lung segmentation accuracy, we propose a new boundary correction method for lung parenchyma. Firstly, the top-hat filter is used to enhance the image contrast; Secondly, we employ the Ostu algorithm for image binarization; Thirdly, the connected component labeling algorithm is utilized to remove the main trachea; Fourthly, the initial mask image is obtained by morphological region filling algorithm; Fifthly, the boundary tracing algorithm is applied to extract the initial lung contour; Afterwards, we design a sudden change degree algorithm to modify the initial lung contour; Finally, the complete lung parenchyma image is obtained. The novelty is that sudden change degree algorithm can detect the inflection points more accurately than other methods, which contributes to repairing lung contour efficiently. The experimental results show that the proposed method can incorporate the juxtapleural nodules into the lung parenchyma effectively, and the precision is increased by 6.46% and 2.72% respectively compared with the other two methods, providing favorable conditions for the accurate detection of pulmonary nodules and having important clinical value.

Citation Download Citation

Junfang Liang, Huiqin Jiang, Ling Ma, Yumin Liu, and Nakaguchi Toshiya "A new boundary correction method for lung parenchyma", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022529 (8 February 2017); https://doi.org/10.1117/12.2266086

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