A New Approach on Skull Stripping of Brain MRI based on Saliency Detection using Dictionary Learning and Sparse Coding
DOI:
https://doi.org/10.15665/rp.v17i2.2050Keywords:
Skull stripping, MRI, Saliency detection, Dictionary learning, Sparse codingAbstract
In brain magnetic resonance images (brain MRI) analysis, for diagnosing certain brain conditions, it is necessary to quantify the brain tissue, which implies to separate the brain from extracranial or non-brain tissues through a process of isolation known as skull stripping. This is a non-trivial task since different types of tissues may have the same gray level, and during the separation process, some brain tissues could be removed. This paper presents a new solution approach for the skull stripping problem, based on saliency detection using dictionary learning and sparse coding, which can operate over T1 and T2 weighted axial brain MRI. Our method first subdivides the axial MRI into full overlapped patches and runs a dictionary learning over them for obtaining its sparse representation. Then, by analyzing the sparse coding matrix, we compute how many patches a dictionary atom affects to classify them as frequent or rare. Then, we calculate the saliency map of the axial MRI according to the composition of the image patches, i.e. an image patch is considered salient if it is mainly composed of frequent atoms, an atom is frequent whether it affects many patches. The non-salient pixels, corresponding to non-brain tissues, are eliminated from the MRI. Numerical results validate our methodReferences
J. V. Manjón, "Segmentación Robusta de Imágenes de RM cerebral," Universidad Politécnica de Valencia, Valencia - España, 2006.
P. Kalavathi and V. Surya Prasath, "Methods on Skull Stripping of MRI Head Scan Images—a Review," Journal of Digital Imaging, vol. 29, no. 3, p. 365–379, 2016.
C. Cecere, C. Corrado and R. Polikar, "Diagnostic Utility of EEG Based Biomarkers for Alzheimer’s Disease," in Annual Northeast Bioengineering Conference (NEBEC), Boston, USA, 2014.
B. S. Mahanand, S. Babu and S. Suresh, "Identification of imaging biomarkers responsible for Alzheimer's Disease using a McRBFN classifier," in International Conference on Cognitive Computing and Information Processing (CCIP), Noida, India, 2015.
K. Dillon, C. Vince and Y.-P. Wang, "A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI," Journal of Neuroscience Methods, vol. 276, pp. 46-55, 2017.
E. M. Meisenzahl, N. Koutsouleris, R. Bottlender, J. J. M. Scheuerecker, S. J. Teipel, S. Holzinger, T. Frodl, U. Preuss, G. Schmitt, B. Burgermeister, M. Reiser, C. Born and H. J. Möller, "Structural brain alterations at different stages of schizophrenia: A voxel-based morphometric study," Schizophrenia Research, vol. 104, no. 1-3, pp. 44-60, 2008.
E. H. Aylward, "Change in MRI striatal volumes as a biomarker in preclinical Huntington’s disease," Brain Research Bulletin, vol. 72, p. 152–158, 2007.
A. Plerou, C. Bobori and P. Vlamos, "Molecular Basis of Huntington’s Disease and Brain Imaging Evidence," in IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Abu Dhabi, UAE, 2015.
L. Wang, Y. Chen, X. Pan, X. Hong and D. 0 Xia, "Level set segmentation of brain magnetic resonance images based on local gaussian distribution fitting energy," Journal of Neuroscience Methods, vol. 188, no. 2, p. 316–325, 2010.
S. Roy and P. Maji, "An accurate and robust skull stripping method for 3-D magnetic resonance brain images," Magnetic Resonance Imaging, vol. 54, pp. 46-57, 2018.
S. Roy, J. A. Butman and D. L. Pham, "Robust skull stripping using multiple MR image contrasts insensitive to pathology," NeuroImage, vol. 146, no. 1, pp. 132-147, 2017.
J. Kleesiek, G. Urban, A. Hubert, D. Schwarz, K. Maier-Hein, M. Bendszus and A. Biller, "Deep MRI brain extraction: A 3D convolutional neural network for skull stripping," NeuroImage, vol. 129, no. 1, pp. 460-469, 2016.
R. Shaswati and M. Pradipta, "A simple skull stripping algorithm for brain MRI," in Eighth International Conference on Advances in Pattern Recognition (ICAPR), Kolkata, India, 2015.
K. Somasundaram and P. Kalavathi, "Contour-based brain segmentation method for magnetic resonance imaging human head scans," Journal of Computer Assisted Tomography, vol. 37, no. 3, p. 353–368, 2013.
M. Brummer, R. Mersereau, R. Eisner and R. Lewine, "Automatic detection of brain contours in MRI data sets," IEEE Transactions on Medical Imaging, vol. 12, no. 2, pp. 153-166, 1993.
S. Roy and P. Maji, "A Simple Skull Stripping Algorithm for Brain MRI," in Eighth International Conference on Advances in Pattern Recognition (ICAPR), Kolkata, India, 2015.
J. Kleesiek, G. Urban, A. Hubert, D. Schwarz, K. Maier-Hein, M. Bendszus and A. Biller, "Deep MRI Brain Extraction: A 3D Convolutional Neural Network for Skull Stripping," NeuroImage, vol. 129, no. 1, pp. 460-469, 2016.
S. Goferman, L. Zelnik-Manor and A. Tal, "Context-Aware Saliency Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 10, pp. 1915 - 1926, 2012.
K. Guo and H.-T. Chen, "Learning sparse dictionaries for saliency detection," in Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), Hollywood, CA, USA, 2012.
N. Li, B. Sun and J. Yu, "A weighted sparse coding framework for saliency detection," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015.
J. Yang and M.-H. Yang, "Top-Down Visual Saliency via Joint CRF and Dictionary Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 3, p. 576–588, 2017.
R. Cong, J. Lei, H. Fu, M.-M. Cheng, W. Lin and Q. Huang, "Review of Visual Saliency Detection with Comprehensive Information," IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, pp. 1-19, 2018.
W. Zhu, S. Liang, W. Yichen and J. Sun, "Saliency Optimization from Robust Background Detection," Columbus, OH, USA, 2014.
I. Rish, "Functional MRI Analysis with Sparse Models," in Joint European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2013, Prague, 2013.
M. Liu, D. Zhang, D. Shen and T. A. D. N. Initiative, "Ensemble sparse classification of Alzheimer's disease," NeuroImage, vol. 60, pp. 1106-1116, 2012.
C. Bao and H. Ji, "Dictionary Learning for Sparse Coding: Algorithms and Convergence Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 7, pp. 1356 - 1369, 2016.
M. Aharon, M. Elad and A. M. Bruckstein, "The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation," IEEE Transactions On Signal Processing, vol. 54, no. 11, pp. 4311-4322, 2006.
M. Aharon, M. Elad and A. M. Bruckstein, "On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them," Journal of Linear Algebra and Applications, vol. 416, pp. 48-67, 2006.
C. M. Institute and I. N. D.-S. I. (INDI), "1000 Functional Connectomes Project," Child Mind Institute & International Neuroimaging Data-Sharing Initiative (INDI), 2017. [Online]. Available: http://fcon_1000.projects.nitrc.org/. [Accessed 20 8
.
MIDAS, "Designed Database of MR Brain Images of Healthy Volunteers," MIDAS, 2010. [Online]. Available: http://insight-journal.org/midas/community/view/21. [Accessed 7 10 2018].
S. M. Smith, "Fast robust automated brain extraction," Human Brain Mapping, vol. 17, no. 3, pp. 143-155, 2002.
D. W. Shattuck and R. M. Leahy, "BrainSuite: an automated cortical surface identification tool," Medical Image Analysis, vol. 6, no. 2, pp. 129-142, 2002.
J. E. Iglesias, C. Y. Liu and P. M. T. Z. Thompson, "Robust brain extraction across datasets and comparison with publicly available methods," IEEE Transactions on Medical Imaging, vol. 30, no. 9, pp. 1617-1634, 2011.
Downloads
Published
Issue
Section
License
The authors to publish in this journal agree to the following conditions:
- The authors transfer the copyright and give the the journal first publication right of the work registered with Creative Commons Attribution License, which allows third parties to use the published work on the condition of always mentioning the authorship and first publication in this journal.
- The authors may perform other independent and additional contractual arrangements for the non-exclusive distribution of the version of the article published in this issue (E.g., Inclusion in an institutional repository or publication in a book), it must be indicated clearly that the work was first published in this journal.
- It allows and encourages the authors to publish their work online (eg institutional or personal pages) before and during the review and publication process. It can lead to productive exchanges and greater and faster dissemination of the published work (see The Effect of Open Access)
Instructions to fill out Certificate of Originality and Copyright Assignment
- Click here and get the forms of Certificate of Originality and Copyright Assignment .
- In each field to fill out, click and complete the corresponding information.
- Once the fields are filled out, at the end of the form copy your scanned signature or digital signature. Please adjust the size of the signature on the form.
- Finally, you can save them as pdf files and send them through the OJS platform as an attachment.