International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Detection of Lung Nodule on Temporal Subtraction Images Based on Artificial Neural Network

  • Received : 2012.03.01
  • Accepted : 2012.06.20
  • Published : 2012.06.25
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Abstract

The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.

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References

  1. K. Doi, "Current status and future potential of computeraided diagnosis in medical imaging," The British Journal of Radiology, vol. 78, pp. S3-S19, 2005. https://doi.org/10.1259/bjr/63353075
  2. M.L. Giger, K. Doi, H. MacMahon, "Image feature analysis and computer-aided diagnosis in digital radiography: Automated detection of nodules in peripheral lung fields," Medical Physics, vol. 15, no. 2, pp.158-166, 1988. https://doi.org/10.1118/1.596247
  3. S. Katsuragawa, K. Doi, H. MacMahon, "Image feature analysis and computer-aided diagnosis in digital radiography: Detection and characterization of interstitial lung disease in digital chest radiographs," Medical Physics, vol. 15, no. 3, pp. 311-319, 1988. https://doi.org/10.1118/1.596224
  4. A.Kano, K.Doi, H.MaxMahon et al. : "Digital image subtraction of temporally sequential chest images for detection of interval change," Med. Phy., vol.21, no.3, pp.453-461(1994). https://doi.org/10.1118/1.597308
  5. T.Ishida , K.Ashizawa , R.Engelmanm et al. : "Application of temporal subtraction for detection of subtraction images using automated initial image matching," Journal of Digital Imaging, vol.12, no.2, pp.77-86 (1999). https://doi.org/10.1007/BF03168846
  6. T.Ishida, S.Katsuragawa, K. Nakamura et al.："Iterative image warping technique for temporal subtraction of sequential chest radiographs to detect interval change," Med. Phys., vol.26, no.7, pp.1320-1329(1999). https://doi.org/10.1118/1.598627
  7. S. Kakeda, K. Nakamura, K. Kamada, H. Watanabe, H. Nakata, S. Katsuragawa, K. Doi, "Improved Detection of Lung Nodules by Using a Temporal Subtraction Technique," Radiology, vol. 224, no. 1, pp. 145-151, 2002. https://doi.org/10.1148/radiol.2241010719
  8. S. Kakeda, K. Kamada, Y. Hatakeyama, T. Aoki, Y. Korogi, S. Katsuragawa, K. Doi, "Effect of Temporal Subtraction Technique on Interpretation Time and Diagnostic Accuracy of Chest Radiography," American Journal of Roentgenology, vol. 187, no. 5, pp. 1253-1259, 2006. https://doi.org/10.2214/AJR.05.1270
  9. Takao H, Doi I, Watanabe T, Tateno M: Temporal subtraction of thin-section thoracic computed tomography based on a 3-dimensional nonlinear geometric warping technique, J. Computer Assisted Tomography 30:283-286, 2006. https://doi.org/10.1097/00004728-200603000-00023
  10. Li Q, Katsuragawa S, Doi K: Improved contralateral subtraction images by use of elastic matching technique, Med. Phys 27:1934-1943, 2000. https://doi.org/10.1118/1.1287112
  11. Y. Itai, H. Kim, S. Ishikawa, S. Katsuragawa and K. Doi, "A new registration method with voxel-matching technique for temporal subtraction images," Proc. SPIE 6915:691531(2008).
  12. Kim, Miyake, Tan, Ishikawa, Murakami, Aoki: "A Method for Reduction of Subtraction Artifacts on Temporal Subtraction Images by Use of Generalized Gradient Vector Flow Technique," Joint Meeting combining The 3rd meeting of Japanese Society of Pulmonary Functional Imaging and 5th International Workshop for Pulmonary Functional Imaging, p.162 (2011).
  13. Itai Y, Kim H, Ishikawa S, Ishida T, Kawashita I, Awai K, Li Q, Doi K: 3D elastic matching for temporal subtraction employing thorax MDCT image, Proc. of the World Congress on Med. Phys. and Biomedical Engineering: 2181-2191, 2006.
  14. Itai Y. Kim H, Ishiwaka S, Katsuragawa S, Ishida T, Doi K: Development of temporal subtraction multislice CT images by using a 3D local matching with a genetic algorithm, Proc. The 92nd Radiological Society of North America: 779, 2006.
  15. C. Xu et al., "Generalized Gradient Vector Flow External Forces for Active Contours," Sig. Proc. An Intl. Jour., 71(2), pp.131-139(1998).
  16. H.Kobakake and S.Hashimoto, "Convergence index filter for vector fields," IEEE Trans. Image Process., vol.8, no.8, pp.1029-1038(1999). https://doi.org/10.1109/83.777084
  17. J.Wei, Y.Hagihara, and H.Kobatake, "Detection of cancerous tumors on chest X-ray images - Candidate detection filter and its application," Proc. ICIP, AP4.2(1999).

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