Two novel methods for juxta-pleural nodule segmentation based on CT images

Shou-liang Qi; Guanglei Si; Han van Triest; Yong Yue

doi:10.1117/12.914043

1 October 2011 Two novel methods for juxta-pleural nodule segmentation based on CT images

Shou-liang Qi, Guanglei Si, Han van Triest, Yong Yue

Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 828539 (2011) https://doi.org/10.1117/12.914043
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt

Abstract

The shape, size and growth rate of lung nodules are the most important indicators for the malignancy of a lung cancer and the basis for assessment of lung cancer treatment effect. Therefore, accurate segmentation of the lung nodules is of great significance for the diagnosis and treatment of the lung cancers. In this paper, two novel methods are proposed to extract juxta-pleural nodules in CT image data for subsequent volume assessment. The algorithm takes the form of user interaction process, such as the selection of the seed point and the adjustment of the volume of interest, which can make best use of the knowledge of the radiologists. The first method combining contour finding and arc chord ratio thresholding and the second method combining the ray casting and line fitting are both designed for segmentation of the juxta-pleural nodules. The algorithm is tested on datasets from 39 patients with a total of 53 juxta-pleural nodules. Evaluated by the senior radiologists, the two methods both gained satisfactory results with segmentation accuracy exceeding 90% on average. It shows the algorithm is helpful for the segmentation, volume measurements and evaluation of juxta-pleural nodules.

Citation Download Citation

Shou-liang Qi, Guanglei Si, Han van Triest, and Yong Yue "Two novel methods for juxta-pleural nodule segmentation based on CT images", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828539 (1 October 2011); https://doi.org/10.1117/12.914043

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