# American Institute of Mathematical Sciences

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

 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.
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 and Imaging, 2014, 8 (2) : 491-505. doi: 10.3934/ipi.2014.8.491
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##### References:
 [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 and 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 and 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 and Optimization, 2020, 10 (2) : 177-206. 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] Qian Zhang, Huaicheng Yan, Jun Cheng, Xisheng Zhan, Kaibo Shi. Fault detection filtering for continuous-time singular systems under a dynamic event-triggered mechanism. Discrete and Continuous Dynamical Systems - S, 2022  doi: 10.3934/dcdss.2022023 [6] Tim McGraw, Baba Vemuri, Evren Özarslan, Yunmei Chen, Thomas Mareci. Variational denoising of diffusion weighted MRI. Inverse Problems and Imaging, 2009, 3 (4) : 625-648. doi: 10.3934/ipi.2009.3.625 [7] Jean-Pierre Françoise, Hongjun Ji. The stability analysis of brain lactate kinetics. Discrete and Continuous Dynamical Systems - S, 2020, 13 (8) : 2135-2143. doi: 10.3934/dcdss.2020182 [8] Yangjin Kim, Khalid Boushaba. An enzyme kinetics model of tumor dormancy, regulation of secondary metastases. Discrete and Continuous Dynamical Systems - S, 2011, 4 (6) : 1465-1498. doi: 10.3934/dcdss.2011.4.1465 [9] Mario Lefebvre. A stochastic model for computer virus propagation. Journal of Dynamics and Games, 2020, 7 (2) : 163-174. doi: 10.3934/jdg.2020010 [10] George Dassios, Michalis N. Tsampas. Vector ellipsoidal harmonics and neuronal current decomposition in the brain. Inverse Problems and Imaging, 2009, 3 (2) : 243-257. doi: 10.3934/ipi.2009.3.243 [11] 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 [12] Monika Muszkieta. A variational approach to edge detection. Inverse Problems and Imaging, 2016, 10 (2) : 499-517. doi: 10.3934/ipi.2016009 [13] Michael Dellnitz, O. Junge, B Thiere. The numerical detection of connecting orbits. Discrete and Continuous Dynamical Systems - B, 2001, 1 (1) : 125-135. doi: 10.3934/dcdsb.2001.1.125 [14] 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 [15] Xianchao Xiu, Lingchen Kong. Rank-one and sparse matrix decomposition for dynamic MRI. Numerical Algebra, Control and Optimization, 2015, 5 (2) : 127-134. doi: 10.3934/naco.2015.5.127 [16] Ryan Compton, Stanley Osher, Louis-S. Bouchard. Hybrid regularization for MRI reconstruction with static field inhomogeneity correction. Inverse Problems and Imaging, 2013, 7 (4) : 1215-1233. doi: 10.3934/ipi.2013.7.1215 [17] Yuyuan Ouyang, Yunmei Chen, Ying Wu. Total variation and wavelet regularization of orientation distribution functions in diffusion MRI. Inverse Problems and Imaging, 2013, 7 (2) : 565-583. doi: 10.3934/ipi.2013.7.565 [18] Micol Amar, Andrea Braides. A characterization of variational convergence for segmentation problems. Discrete and Continuous Dynamical Systems, 1995, 1 (3) : 347-369. doi: 10.3934/dcds.1995.1.347 [19] Fan Jia, Xue-Cheng Tai, Jun Liu. Nonlocal regularized CNN for image segmentation. Inverse Problems and Imaging, 2020, 14 (5) : 891-911. doi: 10.3934/ipi.2020041 [20] 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

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