MR Image Segmentation Based on Contrast Enhancement with Collaborative Learning
AbstractMagnetic resonance imaging (MRI) has been widely used on clinical applications. Due to different weighting strategies MRI, different weighting images have different contrast even for the same anatomy structure of the same subject. Since some MR brain images have low contrast, different types of tissues such as white matter, grey matter, and cerebrospinal fluid (CSF) are difficult to be divided and segmented. Image contrast needs to be enhanced for better post-processing and image analysis. In this paper, a two-step MR brain image segmentation technique is proposed to solve low contrast MR image segmentation problem. A collaborative learning based image enhancement is firstly applied on low contrast MR brain image. Then, spectral clustering algorithm is used for segmenting enhanced image. Experimental results illustrate that the proposed 2-step segmentation method is able to identify boundaries between tissues well, so that MR image segmentation accuracy is improved in compared to image segmentation without contrast enhancement and exact histogram equalization enhanced image segmentation.
(1) Haacke, E.M., et al., Magnetic resonance imaging: physical principles and sequence design. Wiley-Liss, 1999.
(2) El-Baz, A., et al., Biomedical image segmentation: advances and trends. CRC Press, 2016.
(3) Stark J.A., Adaptive image contrast enhancement using generalizations of histogram equalization. Image Processing, IEEE Transactions on, 2000. 9(5): p. 889-896.
(4) Ibrahim, H., et al., Brightness preserving dynamic histogram equalization for image contrast enhancement. Consumer Electronics, IEEE Transactions on, 2007. 53(4): p. 1752-1758.
(5) Jen, T.C., Image contrast enhancement based on intensity-pair distribution. Image Processing, International Conference on, 2005.
(6) Tao, J., et al., Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2000. 22(8): p. 888-905.
(7) Tao, V., et al., Color image segmentation based on mean shift and normalized cuts. Systems, Man, and Cybernetics - Part B: Cybernetics, IEEE Transactions on, 2007. 37(5): p. 1382-1388.
(8) Comaniciu, D., et al., Mean shift: a robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002. 24(5): p. 603-619.
(9) Fowlkes C., et al., Spectral grouping using the Nystrom method. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2004. 26(2): p. 214-225.
(10) Keuchel J., et at., Efficient graph cuts for unsupervised image segmentation using probabilistic sampling and SVD-based approaximation. Statistical and Computational theories of Vision, Computer Vision, International Conference on, 2003.
(11) Chang, Y., et al., Using collaborative learning for image contrast enhancement. Pattern Recognition, International Conference on, 2008.
(12) Zimmerman, J.B., An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. Medical Imaging, IEEE Transactions on, 1988. 7(4): p. 304-312.
(13) Wang, Q., et al., Fast image/video contrast enhancement based on weighted threshold histogram equalization. Consumer Electronics, IEEE Transactions on, 2007. 53(2): p. 757-764.
(14) Yang, S., et al., Constrast enhancement using histogram equalization with bin underflow and bin overflow . Image Processing, International Conference on, 2003.
(15) Kim, Y., Contrast enhancement using brightness preserving bi-histogram equalization. Consumer Electronics, IEEE Transactions on, 2007. 43(1): p. 1-8.
(16) Wang, Y., Image enhancement based on equal area dualistic sub-image histogram equalization method. Consumer Electronics, IEEE Transactions on, 2007. 45(1): p. 68-75.
(17) Chen, S., et al. Minimum mean brightness error bi-histogram equalization in contrast enhancement. Consumer Electronics, IEEE Transactions on, 2003. 49(4): p. 1310-1319.
(18) Kim, W.K., et al., Contrast enhancement using histogram equalization based on logarithmic mapping. Optical Engineering, 2012. 51(6): p. 1-10.
(19) Celik, T., Spatial entropy-based global and local image contrast enhancement. Image Processing, IEEE Transactions on, 2014. 23(12): p. 5298-5308.
(20) Coltuc, D., et al., Exact histogram specification. Image Processing, IEEE Transactions on, 2006. 15(5): p. 1143-1152.
Authors wishing to include figures, tables, or text passages that have already been published elsewhere are required to obtain permission from the copyright owner(s) for both the print and online format and to include evidence that such permission has been granted when submitting their papers. Any material received without such evidence will be assumed to originate from the authors.
All authors of manuscripts accepted for publication in the journal Transactions on Networks and Communications are required to license the Scholar Publishing to publish the manuscript. Each author should sign one of the following forms, as appropriate:
License to publish; to be used by most authors. This grants the publisher a license of copyright. Download forms (MS Word formats) - (doc)
Publication agreement — Crown copyright; to be used by authors who are public servants in a Commonwealth country, such as Canada, U.K., Australia. Download forms (Adobe or MS Word formats) - (doc)
License to publish — U.S. official; to be used by authors who are officials of the U.S. government. Download forms (Adobe or MS Word formats) – (doc)
The preferred method to submit a completed, signed copyright form is to upload it within the task assigned to you in the Manuscript submission system, after the submission of your manuscript. Alternatively, you can submit it by email firstname.lastname@example.org