Skip to main content
Erschienen in: International Journal of Computer Assisted Radiology and Surgery 3/2016

01.03.2016 | Original Article

Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images

verfasst von: Ashis Kumar Dhara, Sudipta Mukhopadhyay, Pramit Saha, Mandeep Garg, Niranjan Khandelwal

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 3/2016

Einloggen, um Zugang zu erhalten

Abstract

Purpose

Boundary roughness of a pulmonary nodule is an important indication of its malignancy. The irregularity of the shape of a nodule is represented in terms of a few diagnostic characteristics such as spiculation, lobulation, and sphericity. Quantitative characterization of these diagnostic characteristics is essential for designing a content-based image retrieval system and computer-aided system for diagnosis of lung cancer.

Methods

This paper presents differential geometry-based techniques for computation of spiculation, lobulation, and sphericity using the binary mask of the segmented nodule. These shape features are computed in 3D considering complete nodule.

Results

The performance of the proposed and competing methods is evaluated in terms of the precision, mean similarity, and normalized discounted cumulative gain on 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The proposed methods are comparable to or better than gold standard technique. The reproducibility of proposed feature extraction techniques is evaluated using RIDER coffee break data set. The mean and standard deviation of the percent change of spiculation, lobulation, and sphericity are \(1.66\pm 2.36\), \(10.57\pm 11.63\), and \(6.27\pm 7.99\) %, respectively.

Conclusion

The prior works of computation of spiculation, lobulation, and sphericity require a set of four ground truths from radiologists and, hence, can not be used in practice. The proposed methods do not require ground truth information of nodules from radiologists, and hence, it can be used in real-life computer-aided diagnosis system for lung cancer.
Literatur
1.
2.
Zurück zum Zitat Diederich S, Wormanns D, Semik M, Thomas M, Lenzen H, Roos N, Heindel W (2002) Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 222(3):773–781CrossRefPubMed Diederich S, Wormanns D, Semik M, Thomas M, Lenzen H, Roos N, Heindel W (2002) Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 222(3):773–781CrossRefPubMed
3.
Zurück zum Zitat Ko JP, Naidich DP (2004) Computer-aided diagnosis and the evaluation of lung disease. J Thorac Imaging 19(3):136–155CrossRefPubMed Ko JP, Naidich DP (2004) Computer-aided diagnosis and the evaluation of lung disease. J Thorac Imaging 19(3):136–155CrossRefPubMed
4.
Zurück zum Zitat Ost D, Fein AM, Feinsilver SH (2003) The solitary pulmonary nodule. N Engl J Med 348(25):2535–2542CrossRefPubMed Ost D, Fein AM, Feinsilver SH (2003) The solitary pulmonary nodule. N Engl J Med 348(25):2535–2542CrossRefPubMed
5.
Zurück zum Zitat Horsthemke WH, Raicu DS, Furst JD (2009) Characterizing pulmonary nodule shape using a boundary-region approach. In: Proceedings of SPIE medical imaging 2009, vol 7260. Florida, pp 72602Y–72602Y-9 Horsthemke WH, Raicu DS, Furst JD (2009) Characterizing pulmonary nodule shape using a boundary-region approach. In: Proceedings of SPIE medical imaging 2009, vol 7260. Florida, pp 72602Y–72602Y-9
6.
Zurück zum Zitat Raicu DS, Varutbangkul E, Cisneros JG, Furst JD, Channin DS, Armato SG III (2007) Semantics and image content integration for pulmonary nodule interpretation in thoracic computed tomography. In: Proceedings of SPIE medical imaging 2007, pp 65120S–65120S-12 Raicu DS, Varutbangkul E, Cisneros JG, Furst JD, Channin DS, Armato SG III (2007) Semantics and image content integration for pulmonary nodule interpretation in thoracic computed tomography. In: Proceedings of SPIE medical imaging 2007, pp 65120S–65120S-12
7.
Zurück zum Zitat Dhara AK, Mukhopadhyay S, Alam N, Khandelwal N (2013) Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. In: SPIE medical imaging 2013: computer aided diagnosis, vol 8670. Florida, pp 86700K–86700K-6 Dhara AK, Mukhopadhyay S, Alam N, Khandelwal N (2013) Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. In: SPIE medical imaging 2013: computer aided diagnosis, vol 8670. Florida, pp 86700K–86700K-6
8.
Zurück zum Zitat Teague MR (1980) Image analysis via the general theory of moments. J Opt Soc Am 70(8):920–930CrossRef Teague MR (1980) Image analysis via the general theory of moments. J Opt Soc Am 70(8):920–930CrossRef
9.
Zurück zum Zitat McNitt-Gray MF, Armato SG III, Meyer CR, Reeves AP, McLennan G, Pais RC, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing PYD, Yankelevitz DF, Aberle DR, Beek EJR, MacMahon H, Kazerooni EA, Croft BY, Clarke LP (2007) The lung image database consortium LIDC data collection process for nodule detection and annotation. Acad Radiol 14(12):1464–1474 McNitt-Gray MF, Armato SG III, Meyer CR, Reeves AP, McLennan G, Pais RC, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing PYD, Yankelevitz DF, Aberle DR, Beek EJR, MacMahon H, Kazerooni EA, Croft BY, Clarke LP (2007) The lung image database consortium LIDC data collection process for nodule detection and annotation. Acad Radiol 14(12):1464–1474
10.
Zurück zum Zitat Dhara AK, Mukhopadhyay S, Das Gupta R, Garg M, Khandelwal N (2015) A segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging. doi:10.1007/s10278-015-9812-6 Dhara AK, Mukhopadhyay S, Das Gupta R, Garg M, Khandelwal N (2015) A segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging. doi:10.​1007/​s10278-015-9812-6
11.
Zurück zum Zitat Tsai DM, Hou HT, Su HJ (1999) Boundary-based corner detection using eigenvalues of covariance matrices. Pattern Recogn Lett 20(1):31–40CrossRef Tsai DM, Hou HT, Su HJ (1999) Boundary-based corner detection using eigenvalues of covariance matrices. Pattern Recogn Lett 20(1):31–40CrossRef
12.
Zurück zum Zitat Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggr Comput Graph 21:163–169CrossRef Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggr Comput Graph 21:163–169CrossRef
13.
Zurück zum Zitat Koenderink JJ, van Doorn AJ (1992) Surface shape and curvature scales. Image Vis Comput 10(8):557–564CrossRef Koenderink JJ, van Doorn AJ (1992) Surface shape and curvature scales. Image Vis Comput 10(8):557–564CrossRef
14.
Zurück zum Zitat Dong C, Wang G (2005) Curvatures estimation on triangular mesh. J Zhejiang Univ Sci 6(1):128–136CrossRef Dong C, Wang G (2005) Curvatures estimation on triangular mesh. J Zhejiang Univ Sci 6(1):128–136CrossRef
15.
Zurück zum Zitat Sladoje N, Nyström I, Saha PK (2005) Measurements of digitized objects with fuzzy borders in 2D and 3D. Image Vis Comput 23(2):123–132CrossRef Sladoje N, Nyström I, Saha PK (2005) Measurements of digitized objects with fuzzy borders in 2D and 3D. Image Vis Comput 23(2):123–132CrossRef
16.
Zurück zum Zitat McNitt-Gray MF, Kim GH, Zhao B, Schwartz LH, Clunie D, Cohen K, Petrick N, Fenimore C, Lu ZJ, Buckler AJ (2015) Determining the variability of lesion size measurements from ct patient data sets acquired under “no change” conditions. Transl Oncol 8(1):55–64PubMedCentralCrossRefPubMed McNitt-Gray MF, Kim GH, Zhao B, Schwartz LH, Clunie D, Cohen K, Petrick N, Fenimore C, Lu ZJ, Buckler AJ (2015) Determining the variability of lesion size measurements from ct patient data sets acquired under “no change” conditions. Transl Oncol 8(1):55–64PubMedCentralCrossRefPubMed
17.
Zurück zum Zitat Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DPY, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931PubMedCentralCrossRefPubMed Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DPY, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931PubMedCentralCrossRefPubMed
18.
Zurück zum Zitat Xu J, Napel S, Greenspan H, Beaulieu CF, Agrawal N, Rubin D (2012) Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval. Med Phys 39(9):5405–5418PubMedCentralCrossRefPubMed Xu J, Napel S, Greenspan H, Beaulieu CF, Agrawal N, Rubin D (2012) Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval. Med Phys 39(9):5405–5418PubMedCentralCrossRefPubMed
19.
Zurück zum Zitat Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Trans Inf Syst 20(4):422–446CrossRef Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Trans Inf Syst 20(4):422–446CrossRef
20.
Zurück zum Zitat Seitz KA Jr, Giuca AM, Furst J, Raicu D (2012) Learning lung nodule similarity using a genetic algorithm. In: Proceedings of SPIE medical imaging 2012, vol 8315. San Deigo, USA, pp 831537–831537-7 Seitz KA Jr, Giuca AM, Furst J, Raicu D (2012) Learning lung nodule similarity using a genetic algorithm. In: Proceedings of SPIE medical imaging 2012, vol 8315. San Deigo, USA, pp 831537–831537-7
21.
Zurück zum Zitat Han F, Wang H, Zhang G, Han H, Song B, Li L, Moore W, Lu H, Zhao H, Liang Z (2014) Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imaging 28(1):99–115PubMedCentralCrossRef Han F, Wang H, Zhang G, Han H, Song B, Li L, Moore W, Lu H, Zhao H, Liang Z (2014) Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imaging 28(1):99–115PubMedCentralCrossRef
22.
Zurück zum Zitat Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI (2003) Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 22(10):1259–1274CrossRefPubMed Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI (2003) Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 22(10):1259–1274CrossRefPubMed
23.
Zurück zum Zitat Reeves AP, Chan AB, Yankelevitz DF, Henschke CI, Kressler B, Kostis WJ (2006) On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 25(4):435–450CrossRefPubMed Reeves AP, Chan AB, Yankelevitz DF, Henschke CI, Kressler B, Kostis WJ (2006) On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 25(4):435–450CrossRefPubMed
24.
Zurück zum Zitat Teo BK, Seo Y, Bacharach SL, Carrasquillo JA, Libutti SK, Shukla H, Hasegawa BH, Hawkins RA, Franc BL (2007) Partial-volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data. J Nucl Med 48(5):802–810PubMed Teo BK, Seo Y, Bacharach SL, Carrasquillo JA, Libutti SK, Shukla H, Hasegawa BH, Hawkins RA, Franc BL (2007) Partial-volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data. J Nucl Med 48(5):802–810PubMed
Metadaten
Titel
Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images
verfasst von
Ashis Kumar Dhara
Sudipta Mukhopadhyay
Pramit Saha
Mandeep Garg
Niranjan Khandelwal
Publikationsdatum
01.03.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 3/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-015-1284-0

Weitere Artikel der Ausgabe 3/2016

International Journal of Computer Assisted Radiology and Surgery 3/2016 Zur Ausgabe

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.