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

Previous Article
A Rellich type theorem for discrete Schrödinger operators
IPI Home
This Issue
Next Article
A stable method solving the total variation dictionary model with $L^\infty$ constraints

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

Abstract
Related Papers

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.
[2]	T. Chan and L. Vese, Active Contours Without Edges,, IEEE Trans. Image Proc., 10 (2001), 266. doi: 10.1109/83.902291.
[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.
[4]	K. Doi, M. L. Giger and K. Doi, Computer-Aided Diagnosis in Medical Imaging,, Elsevier Science Pub, (1999).
[5]	R. Dubey, M. Hanmandlu, S. Gupta and S. Gupta, emi-automatic Segmentation of MRI Brain Tumor,, ICGST-GVIP Journal, 9 (2009), 33.
[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.*
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[15]	D. Pham, C. Xu and J. Prince, Current Methods in medical image segmentation,, Annual Review of Biomedical Engineering, 2 (2000), 315.
[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.
[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.
[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.
[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.
[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.
[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.
[22]	P. Wang, A. DeNunzio, P. Okunieff and W. G. O'Dell, Lung metastases detection using 3d template matching,, Med. Phys., 34 (2007), 915.
[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.*
[24]	C. Wood, Computer aided detection (CAD) for breast MRI,, Technol Cancer Res Treat, 4 (2005), 49.
[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.
[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.

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.
[2]	T. Chan and L. Vese, Active Contours Without Edges,, IEEE Trans. Image Proc., 10 (2001), 266. doi: 10.1109/83.902291.
[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.
[4]	K. Doi, M. L. Giger and K. Doi, Computer-Aided Diagnosis in Medical Imaging,, Elsevier Science Pub, (1999).
[5]	R. Dubey, M. Hanmandlu, S. Gupta and S. Gupta, emi-automatic Segmentation of MRI Brain Tumor,, ICGST-GVIP Journal, 9 (2009), 33.
[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.*
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[15]	D. Pham, C. Xu and J. Prince, Current Methods in medical image segmentation,, Annual Review of Biomedical Engineering, 2 (2000), 315.
[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.
[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.
[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.
[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.
[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.
[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.
[22]	P. Wang, A. DeNunzio, P. Okunieff and W. G. O'Dell, Lung metastases detection using 3d template matching,, Med. Phys., 34 (2007), 915.
[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.*
[24]	C. Wood, Computer aided detection (CAD) for breast MRI,, Technol Cancer Res Treat, 4 (2005), 49.
[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.
[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.

[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]	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
[4]	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
[5]	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
[6]	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
[7]	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
[8]	Monika Muszkieta. A variational approach to edge detection. Inverse Problems & Imaging, 2016, 10 (2) : 499-517. doi: 10.3934/ipi.2016009
[9]	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
[10]	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
[11]	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
[12]	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
[13]	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
[14]	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
[15]	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
[16]	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
[17]	Boguslaw Twarog, Robert Pekala, Jacek Bartman, Zbigniew Gomolka. The changes of air gap in inductive engines as vibration indicator aided by mathematical model and artificial neural network. Conference Publications, 2007, 2007 (Special) : 1005-1012. doi: 10.3934/proc.2007.2007.1005
[18]	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
[19]	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
[20]	Mahdi Jalili. EEG-based functional brain networks: Hemispheric differences in males and females. Networks & Heterogeneous Media, 2015, 10 (1) : 223-232. doi: 10.3934/nhm.2015.10.223

2017 Impact Factor: 1.465

American Institute of Mathematical Sciences