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Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models

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Abstract

Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

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Acknowledgments

This work was supported by a grant from the National Science Council of Taiwan under Contract No. 99-2221-E-130-014 and did not involve human subjects.

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Authors and Affiliations

  1. Department of Healthcare Information and Management, Ming Chuan University, Taoyuan, 333, Taiwan, ROC

    Wen-Li Lee & Koyin Chang

  2. Veterans General Hospital, Kaohsiung, 813, Taiwan, ROC

    Kai-Sheng Hsieh

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  1. Wen-Li Lee
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  2. Koyin Chang
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  3. Kai-Sheng Hsieh
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Correspondence to Wen-Li Lee.

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Lee, WL., Chang, K. & Hsieh, KS. Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models. Med Biol Eng Comput 54, 1409–1422 (2016). https://doi.org/10.1007/s11517-015-1412-6

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  • DOI: https://doi.org/10.1007/s11517-015-1412-6

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