Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-04-2016 | Original Article

A novel bone suppression method that improves lung nodule detection

Suppressing dedicated bone shadows in radiographs while preserving the remaining signal

Authors: Jens von Berg, Stewart Young, Heike Carolus, Robin Wolz, Axel Saalbach, Alberto Hidalgo, Ana Giménez, Tomás Franquet

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2016

Abstract

Purpose

Suppressing thoracic bone shadows in chest radiographs has been previously reported to improve the detection rates for solid lung nodules, however at the cost of increased false detection rates. These bone suppression methods are based on an artificial neural network that was trained using dual-energy subtraction images in order to mimic their appearance.

Method

Here, a novel approach is followed where all bone shadows crossing the lung field are suppressed sequentially leaving the intercostal space unaffected. Given a contour delineating a bone, its image region is spatially transferred to separate normal image gradient components from tangential component. Smoothing the normal partial gradient along the contour results in a reconstruction of the image representing the bone shadow only, because all other overlaid signals tend to cancel out each other in this representation.

Results

The method works even with highly contrasted overlaid objects such as a pacemaker. The approach was validated in a reader study with two experienced chest radiologists, and these images helped improving both the sensitivity and the specificity of the readers for the detection and localization of solid lung nodules. The AUC improved significantly from 0.596 to 0.655 on a basis of 146 images from patients and normals with a total of 123 confirmed lung nodules.

Conclusion

Subtracting all reconstructed bone shadows from the original image results in a soft image where lung nodules are no longer obscured by bone shadows. Both the sensitivity and the specificity of experienced radiologists increased.

Available only for authorised users

Images publicly available from http://www.jsrt.or.jp/jsrt-db/eng.php.

Images publicly available from http://cancerimagingarchive.net.

Ahmed B, Rasheed T, Khan MA, Cho SJ, Lee S, Kim TS (2007) Rib suppression for enhancing frontal chest radiographs using independent component analysis. In: Beliczynski B, Dzielinski A, Iwanowski M, Ribeiro B (eds) Adaptive and natural computing algorithms. Springer, Berlin, pp 300–308

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, van 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):915–931CrossRefPubMedPubMedCentral

DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

Freedman MT, Lo SCB, Seibel JC, Bromley CM (2011) Lung nodules: improved detection with software that suppresses the rib and clavicle on chest radiographs. Radiology 260(1):265–273CrossRefPubMed

Hogeweg L, Sanchez C, van Ginneken B (2013) Suppression of translucent elongated structures: applications in chest radiography. IEEE Trans Med Imaging 32(11):2099–2113

Hogeweg LE, Mol C, de Jong PA, van Ginneken B (2010) Rib suppression in chest radiographs to improve classification of textural abnormalities. In: Proceedings of the SPIE medical imaging, vol 7624

Horváth Á, Orbán GG, Horváth Á, Horváth G (2013) An X-ray CAD system with ribcage suppression for improved detection of lung lesions. Electr Eng Comput Sci 57(1):19–33

Horváth G, Orbán G, Horváth Á, Simkó G, Pataki B, Máday P, Juhász S (2009) A CAD system for screening X-ray chest radiography. In: World congress on medical physics and biomedical engineering, September 7–12, 2009, Springer, Munich, Germany, pp 210–213

Kim S, Pyo HB, Lee SK, Lee S, Park SH (2000) Digital image subtraction of temporally sequential chest images by rib image elimination. In: Proceedings of the 22nd annual international conference of the IEEE engineering in medicine and biology society, vol 3. IEEE, pp 1752–1755

10.

Lee JS, Wang JW, Wu HH, Yuan MZ (2012) A nonparametric-based rib suppression method for chest radiographs. Comput Math Appl 64(5):1390–1399CrossRef

11.

Li F, Engelmann R, Pesce L, Armato SG III, MacMahon H (2012) Improved detection of focal pneumonia by chest radiography with bone suppression imaging. Eur Radiol 22(12):2729–2735CrossRefPubMed

12.

Li F, Engelmann R, Pesce LL, Doi K, Metz CE, MacMahon H (2011) Small lung cancers: improved detection by use of bone suppression imaging—comparison with dual-energy subtraction chest radiography. Radiology 261(3):937–949CrossRefPubMed

13.

Lo SCB, Freedman MT (2012) Changes of nodule detection after radiologists read bone opacity suppressed chest radiography. In: SPIE medical imaging. International Society for Optics and Photonics, pp 83152S–83152S

14.

Loog M, van Ginneken B, Schilham AM (2006) Filter learning: application to suppression of bony structures from chest radiographs. Med Image Anal 10(6):826–840CrossRefPubMed

15.

Novak RD, Novak NJ, Gilkeson R, Mansoori B, Aandal GE (2013) A comparison of computer-aided detection (CAD) effectiveness in pulmonary nodule identification using different methods of bone suppression in chest radiographs. J Digit Imaging 26(4):651–656

16.

Orbán G, Horváth Á, Horváth G (2010) Lung nodule detection on rib eliminated radiographs. In: XII mediterranean conference on medical and biological engineering and computing 2010. Springer, pp 363–366

17.

Schalekamp S, van Ginneken B, van den Berk IAH, Hartmann IJC, Snoeren MM, Odink AE, van Lankeren W, Pegge SAH, Schijf LJ, Karssemeijer N, Schaefer-Prokop CM (2014) Bone suppression increases the visibility of invasive pulmonary aspergillosis in chest radiographs. PloS One 9(10):e108,551CrossRef

18.

Schalekamp S, van Ginneken B, Meiss L, Peters-Bax L, Quekel LG, Snoeren MM, Tiehuis AM, Wittenberg R, Karssemeijer N, Schaefer-Prokop CM (2013) Bone suppressed images improve radiologists detection performance for pulmonary nodules in chest radiographs. Eur J Radiol 82(12):2399–2405CrossRefPubMed

19.

Shah PK, Austin JH, White CS, Patel P, Haramati LB, Pearson GD, Shiau MC, Berkmen YM (2003) Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect 1. Radiology 226(1):235–241CrossRefPubMed

20.

Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu K, Matsui M, Fujita H, Kodera Y, Doi K (2000) Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am J Roentgenol 174(1):71–74CrossRef

21.

Simkó G, Orbán G, Máday P, Horváth G (2009) Elimination of clavicle shadows to help automatic lung nodule detection on chest radiographs. In: 4th European conference of the international federation for medical and biological engineering. Springer, pp 488–491

22.

Suzuki K, Abe H, MacMahon H, Doi K (2006) Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging 25(4):406–416CrossRefPubMed

23.

Szucs-Farkas Z, Schick A, Cullmann JL, Ebner L, Megyeri B, Vock P, Christe A (2013) Comparison of dual-energy subtraction and electronic bone suppression combined with computer-aided detection on chest radiographs: effect on human observers’ performance in nodule detection. Am J Roentgenol 200(5):1006–1013CrossRef

24.

Tanaka R, Sanade M, Oda M, Suzuki M, Sakuta H, Kawashima H, Iida H (2013) Improved accuracy of IGRT with bone suppression image-processing. Int J CARS 8(suppl I):41

25.

Vogelsang F, Weiler F, Dahmen J, Kilbinger M, Wein B, Günther R (1998) Detection and compensation of rib structures in chest radiographs for diagnose assistance. In: Proceedings of the international symposium on medical imaging, vol 3338, p 1

Title: A novel bone suppression method that improves lung nodule detection
Suppressing dedicated bone shadows in radiographs while preserving the remaining signal
Authors: Jens von Berg
Stewart Young
Heike Carolus
Robin Wolz
Axel Saalbach
Alberto Hidalgo
Ana Giménez
Tomás Franquet
Publication date: 01-04-2016
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 4/2016
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-015-1278-y

Keynote webinar | Spotlight on medication adherence

Springer Medicine

A novel bone suppression method that improves lung nodule detection

Suppressing dedicated bone shadows in radiographs while preserving the remaining signal

Abstract

Purpose

Method

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Method

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 4/2016

A fast, accurate and closed-form method for pose recognition of an intramedullary nail using a tracked C-arm

X-ray and optical stereo-based 3D sensor fusion system for image-guided neurosurgery

Development of preoperative planning software for transforaminal endoscopic surgery and the guidance for clinical applications

Intra-operative assessment of fractured articular surfaces in cone beam CT image data

Calibration of mobile-gantry computed tomography for surgical navigation

Enriching 3D optical surface scans with prior knowledge: tissue thickness computation by exploiting local neighborhoods