American Institute of Mathematical Sciences

Advanced
May  2014, 8(2): 491-505. doi: 10.3934/ipi.2014.8.491

A new computer-aided method for detecting brain metastases on contrast-enhanced MR images

Hyeuknam Kwon 1, , Yoon Mo Jung 1, , Jaeseok Park 2, and  Jin Keun Seo 1,

1. 

Department of Computational Science and Engineering, Yonsei University, South Korea, South Korea, South Korea
2. 

Department of Brain and Cognitive Engineering, Korea University, South Korea

Received  February 2012 Revised  February 2013 Published  May 2014

This paper presents a new computer-aided method for detection of brain metastases at early-stage (diameter less than $6$mm) on MR images. The proposed detection method has a high level of sensitivity with a relatively low number of false-positives. The strong detection capability of the method is possible due to a size filtering function that sorts out metastases based on the geometry and size. In experiments, we used whole-brain MR data acquired with a contrast-enhanced black-blood type MR imaging technique, which enables distinction of brain metastases from blood vessels. The proposed method performed highly in analysis of the results of experimental MR data and numerical simulation. Because the proposed method has unique features, it could be used in combination with a complementary pre-existing technique.
Keywords: MRI., filtering function, Computer-aided detection, brain metastases, segmentation.
Mathematics Subject Classification: 00A69, 00A66, 94A0.
Citation: Hyeuknam Kwon, Yoon Mo Jung, Jaeseok Park, Jin Keun Seo. A new computer-aided method for detecting brain metastases on contrast-enhanced MR images. Inverse Problems & Imaging, 2014, 8 (2) : 491-505. doi: 10.3934/ipi.2014.8.491
References:
[1]

R. D. Ambrosini, P. Wang and W. G. O'Dell, Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching,, J. Magn. Reson. Imaging., 31 (2010), 85.  doi: 10.1002/jmri.22009.  Google Scholar

[2]

T. Chan and L. Vese, Active Contours Without Edges,, IEEE Trans. Image Proc., 10 (2001), 266.  doi: 10.1109/83.902291.  Google Scholar

[3]

K. Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential,, Comput Med Imaging Graph Epub, 31 (2007), 198.  doi: 10.1016/j.compmedimag.2007.02.002.  Google Scholar

[4]

K. Doi, M. L. Giger and K. Doi, Computer-Aided Diagnosis in Medical Imaging,, Elsevier Science Pub, (1999).   Google Scholar

[5]

R. Dubey, M. Hanmandlu, S. Gupta and S. Gupta, emi-automatic Segmentation of MRI Brain Tumor,, ICGST-GVIP Journal, 9 (2009), 33.   Google Scholar

[6]

D. Finelli, G. Hurst, R. Gullapali and E. Bellon, Improved contrast of enhancing brain lesions on postgadolinium, T1-weighted spin-echo images with use of magnetization transfer,, Radiology, 190 (1994), 553.   Google Scholar

[7]

C. I. Henschke, D. F. Yankelevitz, I. Mateescu, D. W. Brettle, T. G. Rainey and F. S. Weingard, Neural networks for the analysis of small pulmonary nodules,, Clin Imaging., 21 (1997), 390.  doi: 10.1016/S0899-7071(97)81731-7.  Google Scholar

[8]

J. Jagannathan, J. H. Sherman, G. U. Mehta GU and L. S. Chin, Radiobiology of brain metastasis: Applications in stereotactic radiosurgery,, Neurosurg Focus, 22 (2007), 1.  doi: 10.3171/foc.2007.22.3.5.  Google Scholar

[9]

Y. Lee, T. Hara, H. Fujita, S. Itoh and T. Ishigaki, Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,, IEEE Trans. Med. Imaging, 20 (2001), 595.   Google Scholar

[10]

A. Madabhushi, M. D. Feldman, D. N. Metaxas, J. Tomaszeweski and D. Chute, Automated detection of prostatic adenocarcinoma from high-resolution ex vivo mri,, IEEE Trans. Med. Imaging, 24 (2005), 1611.  doi: 10.1109/TMI.2005.859208.  Google Scholar

[11]

M. F. McNitt-Gray, E. M. Hart, N. Wyckoff, J. W. Sayre, J. G. Goldin and D. R. Aberle, A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution ct: Preliminary results,, Med. Phys., 26 (1999), 880.  doi: 10.1118/1.598603.  Google Scholar

[12]

L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo and J. M. Reinhardt, Breast mri lesion classification: Improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system,, J. Magn. Reson. Imaging, 25 (2007), 89.  doi: 10.1002/jmri.20794.  Google Scholar

[13]

S. Mirowitz, Intracranial lesion enhancement with gadolinium: T1-weighted spin-echo versus three-dimensional Fourier transform gradient-echo MR imaging,, Am. J. Neuroradiol., 20 (1992), 1554.   Google Scholar

[14]

J. Park and E. Y. Kim, Contrast-enhanced, three-dimensional, whole-brain, black-blood imaging: Application to small brain metastases,, Magn. Reson. Med., 63 (2010), 553.  doi: 10.1002/mrm.22261.  Google Scholar

[15]

D. Pham, C. Xu and J. Prince, Current Methods in medical image segmentation,, Annual Review of Biomedical Engineering, 2 (2000), 315.   Google Scholar

[16]

M. Prastawa, E. Bullitt, N. Moon, K. Van Leemput and G. Gerig, Automatic brain tumor segmentation by subject specific modification of atlas priors,, Acad. Radiol., 10 (2003), 1341.   Google Scholar

[17]

M. G. Ranasinghe and J. M. Sheehan, Surgical management of brain metastases,, Neurosurg Focus, 22 (2007), 1.  doi: 10.3171/foc.2007.22.3.3.  Google Scholar

[18]

P. D. Schellinger, H. M. Meinck and A. Thron, Diagnostic accuracy of MRI compared to CCT in patients with brain metastases,, J. Neurooncol., 44 (1999), 275.   Google Scholar

[19]

T. Sugahara, Y. Korogi, Y. Ge, Y. Shigematsu, L. Liang, K. Yoshizumi, M. Kitajima and M. Takahashi, Contrast enhancement of intracranial lesions: Conventional T1-weighted spin-echo versus fast spin-echo MR imaging techniques,, Am. J. Neuroradiol., 20 (1999), 1554.   Google Scholar

[20]

G. Sze, E. Milano, C. Johnson and L. Heier, Detection of brain metastases: Comparison of contrast-enhanced MR with unenhanced MR and enhanced CT,, Am. J. Neuroradiol, 11 (1990), 785.   Google Scholar

[21]

S. Viswanath, B. N. Bloch, E. Genega, N. Rofsky, R. Lenkinski, J. Chappelow, R. Toth and A. Madabhushi, A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI,, Med. Image. Comput. Comput. Assist. Interv., 11 (2008), 662.  doi: 10.1007/978-3-540-85988-8_79.  Google Scholar

[22]

P. Wang, A. DeNunzio, P. Okunieff and W. G. O'Dell, Lung metastases detection using 3d template matching,, Med. Phys., 34 (2007), 915.   Google Scholar

[23]

T. C. Williams, W. B. DeMartini, S. C. Partridge, S. Peacock and C. D. Lehman, Breast MR imaging: Computer-aided evaluation for discriminating benign from malignant lesions,, Radiology, 244 (2007), 94.   Google Scholar

[24]

C. Wood, Computer aided detection (CAD) for breast MRI,, Technol Cancer Res Treat, 4 (2005), 49.   Google Scholar

[25]

A. Yezzi, S. Kichenassaym, A. Kumar, P. Olver and A. Tannenbaum, A geometric snake model for segmentation of medical imagery,, IEEE Trans. Med. Imaging., 16 (1997), 199.  doi: 10.1109/42.563665.  Google Scholar

[26]

B. Zhao, G. Gamsu, M. S. Ginsberg, L. Jiang and L. H. Schwartz, Automatic detection of small lung nodules on ct utilizing a local density maximum algorithm,, J. Appl. Clin. Med. Phys., 4 (2003), 248.   Google Scholar

show all references

References:
[1]

R. D. Ambrosini, P. Wang and W. G. O'Dell, Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching,, J. Magn. Reson. Imaging., 31 (2010), 85.  doi: 10.1002/jmri.22009.  Google Scholar

[2]

T. Chan and L. Vese, Active Contours Without Edges,, IEEE Trans. Image Proc., 10 (2001), 266.  doi: 10.1109/83.902291.  Google Scholar

[3]

K. Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential,, Comput Med Imaging Graph Epub, 31 (2007), 198.  doi: 10.1016/j.compmedimag.2007.02.002.  Google Scholar

[4]

K. Doi, M. L. Giger and K. Doi, Computer-Aided Diagnosis in Medical Imaging,, Elsevier Science Pub, (1999).   Google Scholar

[5]

R. Dubey, M. Hanmandlu, S. Gupta and S. Gupta, emi-automatic Segmentation of MRI Brain Tumor,, ICGST-GVIP Journal, 9 (2009), 33.   Google Scholar

[6]

D. Finelli, G. Hurst, R. Gullapali and E. Bellon, Improved contrast of enhancing brain lesions on postgadolinium, T1-weighted spin-echo images with use of magnetization transfer,, Radiology, 190 (1994), 553.   Google Scholar

[7]

C. I. Henschke, D. F. Yankelevitz, I. Mateescu, D. W. Brettle, T. G. Rainey and F. S. Weingard, Neural networks for the analysis of small pulmonary nodules,, Clin Imaging., 21 (1997), 390.  doi: 10.1016/S0899-7071(97)81731-7.  Google Scholar

[8]

J. Jagannathan, J. H. Sherman, G. U. Mehta GU and L. S. Chin, Radiobiology of brain metastasis: Applications in stereotactic radiosurgery,, Neurosurg Focus, 22 (2007), 1.  doi: 10.3171/foc.2007.22.3.5.  Google Scholar

[9]

Y. Lee, T. Hara, H. Fujita, S. Itoh and T. Ishigaki, Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,, IEEE Trans. Med. Imaging, 20 (2001), 595.   Google Scholar

[10]

A. Madabhushi, M. D. Feldman, D. N. Metaxas, J. Tomaszeweski and D. Chute, Automated detection of prostatic adenocarcinoma from high-resolution ex vivo mri,, IEEE Trans. Med. Imaging, 24 (2005), 1611.  doi: 10.1109/TMI.2005.859208.  Google Scholar

[11]

M. F. McNitt-Gray, E. M. Hart, N. Wyckoff, J. W. Sayre, J. G. Goldin and D. R. Aberle, A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution ct: Preliminary results,, Med. Phys., 26 (1999), 880.  doi: 10.1118/1.598603.  Google Scholar

[12]

L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo and J. M. Reinhardt, Breast mri lesion classification: Improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system,, J. Magn. Reson. Imaging, 25 (2007), 89.  doi: 10.1002/jmri.20794.  Google Scholar

[13]

S. Mirowitz, Intracranial lesion enhancement with gadolinium: T1-weighted spin-echo versus three-dimensional Fourier transform gradient-echo MR imaging,, Am. J. Neuroradiol., 20 (1992), 1554.   Google Scholar

[14]

J. Park and E. Y. Kim, Contrast-enhanced, three-dimensional, whole-brain, black-blood imaging: Application to small brain metastases,, Magn. Reson. Med., 63 (2010), 553.  doi: 10.1002/mrm.22261.  Google Scholar

[15]

D. Pham, C. Xu and J. Prince, Current Methods in medical image segmentation,, Annual Review of Biomedical Engineering, 2 (2000), 315.   Google Scholar

[16]

M. Prastawa, E. Bullitt, N. Moon, K. Van Leemput and G. Gerig, Automatic brain tumor segmentation by subject specific modification of atlas priors,, Acad. Radiol., 10 (2003), 1341.   Google Scholar

[17]

M. G. Ranasinghe and J. M. Sheehan, Surgical management of brain metastases,, Neurosurg Focus, 22 (2007), 1.  doi: 10.3171/foc.2007.22.3.3.  Google Scholar

[18]

P. D. Schellinger, H. M. Meinck and A. Thron, Diagnostic accuracy of MRI compared to CCT in patients with brain metastases,, J. Neurooncol., 44 (1999), 275.   Google Scholar

[19]

T. Sugahara, Y. Korogi, Y. Ge, Y. Shigematsu, L. Liang, K. Yoshizumi, M. Kitajima and M. Takahashi, Contrast enhancement of intracranial lesions: Conventional T1-weighted spin-echo versus fast spin-echo MR imaging techniques,, Am. J. Neuroradiol., 20 (1999), 1554.   Google Scholar

[20]

G. Sze, E. Milano, C. Johnson and L. Heier, Detection of brain metastases: Comparison of contrast-enhanced MR with unenhanced MR and enhanced CT,, Am. J. Neuroradiol, 11 (1990), 785.   Google Scholar

[21]

S. Viswanath, B. N. Bloch, E. Genega, N. Rofsky, R. Lenkinski, J. Chappelow, R. Toth and A. Madabhushi, A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI,, Med. Image. Comput. Comput. Assist. Interv., 11 (2008), 662.  doi: 10.1007/978-3-540-85988-8_79.  Google Scholar

[22]

P. Wang, A. DeNunzio, P. Okunieff and W. G. O'Dell, Lung metastases detection using 3d template matching,, Med. Phys., 34 (2007), 915.   Google Scholar

[23]

T. C. Williams, W. B. DeMartini, S. C. Partridge, S. Peacock and C. D. Lehman, Breast MR imaging: Computer-aided evaluation for discriminating benign from malignant lesions,, Radiology, 244 (2007), 94.   Google Scholar

[24]

C. Wood, Computer aided detection (CAD) for breast MRI,, Technol Cancer Res Treat, 4 (2005), 49.   Google Scholar

[25]

A. Yezzi, S. Kichenassaym, A. Kumar, P. Olver and A. Tannenbaum, A geometric snake model for segmentation of medical imagery,, IEEE Trans. Med. Imaging., 16 (1997), 199.  doi: 10.1109/42.563665.  Google Scholar

[26]

B. Zhao, G. Gamsu, M. S. Ginsberg, L. Jiang and L. H. Schwartz, Automatic detection of small lung nodules on ct utilizing a local density maximum algorithm,, J. Appl. Clin. Med. Phys., 4 (2003), 248.   Google Scholar

[1]

Dominique Zosso, Jing An, James Stevick, Nicholas Takaki, Morgan Weiss, Liane S. Slaughter, Huan H. Cao, Paul S. Weiss, Andrea L. Bertozzi. Image segmentation with dynamic artifacts detection and bias correction. Inverse Problems & Imaging, 2017, 11 (3) : 577-600. doi: 10.3934/ipi.2017027
[2]

Lok Ming Lui, Yalin Wang, Tony F. Chan, Paul M. Thompson. Brain anatomical feature detection by solving partial differential equations on general manifolds. Discrete & Continuous Dynamical Systems - B, 2007, 7 (3) : 605-618. doi: 10.3934/dcdsb.2007.7.605
[3]

Vassilios A. Tsachouridis, Georgios Giantamidis, Stylianos Basagiannis, Kostas Kouramas. Formal analysis of the Schulz matrix inversion algorithm: A paradigm towards computer aided verification of general matrix flow solvers. Numerical Algebra, Control & Optimization, 2019, 0 (0) : 0-0. doi: 10.3934/naco.2019047
[4]

Gerasimos G. Rigatos, Efthymia G. Rigatou, Jean Daniel Djida. Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering. Mathematical Biosciences & Engineering, 2015, 12 (5) : 1017-1035. doi: 10.3934/mbe.2015.12.1017
[5]

Tim McGraw, Baba Vemuri, Evren Özarslan, Yunmei Chen, Thomas Mareci. Variational denoising of diffusion weighted MRI. Inverse Problems & Imaging, 2009, 3 (4) : 625-648. doi: 10.3934/ipi.2009.3.625
[6]

Jean-Pierre Françoise, Hongjun Ji. The stability analysis of brain lactate kinetics. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020182
[7]

Yangjin Kim, Khalid Boushaba. An enzyme kinetics model of tumor dormancy, regulation of secondary metastases. Discrete & Continuous Dynamical Systems - S, 2011, 4 (6) : 1465-1498. doi: 10.3934/dcdss.2011.4.1465
[8]

George Dassios, Michalis N. Tsampas. Vector ellipsoidal harmonics and neuronal current decomposition in the brain. Inverse Problems & Imaging, 2009, 3 (2) : 243-257. doi: 10.3934/ipi.2009.3.243
[9]

Carole Guillevin, Rémy Guillevin, Alain Miranville, Angélique Perrillat-Mercerot. Analysis of a mathematical model for brain lactate kinetics. Mathematical Biosciences & Engineering, 2018, 15 (5) : 1225-1242. doi: 10.3934/mbe.2018056
[10]

Monika Muszkieta. A variational approach to edge detection. Inverse Problems & Imaging, 2016, 10 (2) : 499-517. doi: 10.3934/ipi.2016009
[11]

Michael Dellnitz, O. Junge, B Thiere. The numerical detection of connecting orbits. Discrete & Continuous Dynamical Systems - B, 2001, 1 (1) : 125-135. doi: 10.3934/dcdsb.2001.1.125
[12]

Benjamin Steinberg, Yuqing Wang, Huaxiong Huang, Robert M. Miura. Spatial Buffering Mechanism: Mathematical Model and Computer Simulations. Mathematical Biosciences & Engineering, 2005, 2 (4) : 675-702. doi: 10.3934/mbe.2005.2.675
[13]

Elena Celledoni, Markus Eslitzbichler, Alexander Schmeding. Shape analysis on Lie groups with applications in computer animation. Journal of Geometric Mechanics, 2016, 8 (3) : 273-304. doi: 10.3934/jgm.2016008
[14]

Micol Amar, Andrea Braides. A characterization of variational convergence for segmentation problems. Discrete & Continuous Dynamical Systems - A, 1995, 1 (3) : 347-369. doi: 10.3934/dcds.1995.1.347
[15]

Robert D. Sidman, Marie Erie, Henry Chu. A method, with applications, for analyzing co-registered EEG and MRI data. Conference Publications, 2001, 2001 (Special) : 349-356. doi: 10.3934/proc.2001.2001.349
[16]

Xianchao Xiu, Lingchen Kong. Rank-one and sparse matrix decomposition for dynamic MRI. Numerical Algebra, Control & Optimization, 2015, 5 (2) : 127-134. doi: 10.3934/naco.2015.5.127
[17]

Ryan Compton, Stanley Osher, Louis-S. Bouchard. Hybrid regularization for MRI reconstruction with static field inhomogeneity correction. Inverse Problems & Imaging, 2013, 7 (4) : 1215-1233. doi: 10.3934/ipi.2013.7.1215
[18]

Yuyuan Ouyang, Yunmei Chen, Ying Wu. Total variation and wavelet regularization of orientation distribution functions in diffusion MRI. Inverse Problems & Imaging, 2013, 7 (2) : 565-583. doi: 10.3934/ipi.2013.7.565
[19]

Elena Beretta, Markus Grasmair, Monika Muszkieta, Otmar Scherzer. A variational algorithm for the detection of line segments. Inverse Problems & Imaging, 2014, 8 (2) : 389-408. doi: 10.3934/ipi.2014.8.389
[20]

Liming Zhang, Tao Qian, Qingye Zeng. Edge detection by using rotational wavelets. Communications on Pure & Applied Analysis, 2007, 6 (3) : 899-915. doi: 10.3934/cpaa.2007.6.899

2018 Impact Factor: 1.469

Tools

Metrics

  • PDF downloads (18)
  • HTML views (0)
  • Cited by (2)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences