Automatic Segmentation of Region of Interests in MR Images Using Saliency Information and Active Contours
Magnetic resonance imaging (MRI) is the most clinically used and gifted modality to identify brain abnormalities in individuals who might be at risk for brain cancer. To date, automated brain tumor segmentation from MRI modalities remains a sensitive, computationally expensive, and a demanding task. This paper presents an automated and robust segmentation method to enable investigators to make successful diagnosis and planning of radiosurgery by reducing the risk factor and study duration. The proposed system consists of following steps: (1) remove the non-brain part from MRI, (2) estimate saliency map of MRI, (3) use the salient region (tumor) as an identification marker and segment the salient object by finding the “optimal” closed contour around the tumor. The system has been tested on real patient images with excellent results. The qualitative and quantitative evaluations by comparing with ground truths and with other existing approaches demonstrate the effectiveness of the proposed method.
KeywordsVisual saliency MRI Tumor detection Active contours
This research is supported by: (1) The Industrial Strategic technology development program, 10041772, (The Development of an Adaptive Mixed-Reality Space based on Interactive Architecture) funded by the MKE (Ministry of Knowledge Economy, Korea) and, (2) The Ministry of Knowledge Economy (MKE), Korea, under IT/SW Creative research program supervised by the National IT Industry Promotion Agency (NIPA)” (NIPA-2012-H0502-12-1013).
- 2.Ejaz N, Tariq TB, Baik SW (2012) Adaptive key frame extraction for video summarization using an aggregation mechanism. J Vis Commun Image Represent 23(7):1031–1040Google Scholar
- 5.Wang X, Pang Q (2011) The research on segmentation of complex object. Int Congr Image Signal Process (CISP) 3:1177–1281Google Scholar
- 6.Angelini ED, Clatz O, Emmanuel M, Konukoglu E, Capelle L, Duffau H (2007) Glioma dynamics and computational models: a review of segmentation, registration, and in silico growth algorithms and their clinical applications. J Curr Med Imaging Rev 3(4):262–276(15)Google Scholar
- 8.Vezhnevets V, Konouchine V (2005) “GrowCut”—interactive multi-label N-D image segmentation by cellular automata. Presented at the Graph-icon, Novosibirsk AkademgorodokGoogle Scholar
- 9.Jaffer A, Zia S, Latif G, Mirza AM, Mehmood I, Ejaz N, Baik SW (2012) Anisotropic diffusion based brain MRI segmentation and 3D reconstruction. Int J Comput Intell Syst 5(3):494–504Google Scholar
- 10.Boesen K, Rehm K, Schaper K, Stoltzner S, Woods R, Lüders E, Rottenberg D (2004) Quantitative comparison of four brain extraction algorithms. J Neuroimage 22(3):1255–1261Google Scholar
- 11.Blackwell HR (1946) Contrast thresholds of the human eye. J Opt Soc Am (1917–1983) 36(11):624–632Google Scholar
- 13.Harvard Medical School http://med.harvard.edu/AANLIB/
- 14.Pakistan Institute of Medical Sciences http://www.pims.gov.pk/radiology.htm