American Institute of Mathematical Sciences

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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]

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

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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

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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

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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

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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

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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

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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

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