New tumor detection method using Nl-means filter and histogram study

In this article we treat a very interesting and very important research subject that affects directly the human brain. The subject here is the tumor detection on a brain MRI The tumors evaluation, its progressions and its area, are very important data to assist the doctors in the disease diagnosis. However, tumor observation and image analysis proves to be a delicate exercise requiring a lot of MRI and using multiple acquisitions under different protocols. The hard challenge now is to accurately detect the tumor surface. In this article we present a new method of tumor regions detection on brain MRI. This method consists of three steps: a) filtering using Nl-means filter and extracting the brain from skull using EMBE method b) the MRI image study and histogram analysis, this step will help us to find a threshold of intensity to extract tumor; c) the tumor extraction. We will complete our work by a tumor characterization and determination of the geometric properties.

[1]  T. Mikkelsen,et al.  Cerebral tumor volume calculations using planimetric and eigenimage analysis. , 1996, Medical physics.

[2]  Sandabad Sara,et al.  NEW BRAIN EXTRACTION METHOD USING EXPECTATION MAXIMIZATION AND MATHEMATICAL MORPHOLOGY , 2015 .

[3]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[4]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.

[5]  W E Phillips,et al.  Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. , 1995, Magnetic Resonance Imaging.

[6]  Alan L. Yuille,et al.  Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification , 2008, IEEE Transactions on Medical Imaging.

[7]  Ron Kikinis,et al.  Segmentation of Meningiomas and Low Grade Gliomas in MRI , 1999, MICCAI.

[8]  R. Knobler,et al.  Fast tissue segmentation based on a 4D feature map in characterization of intracranial lesions , 1999, Journal of magnetic resonance imaging : JMRI.

[9]  Nicolaos B. Karayiannis,et al.  Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization , 1999, IEEE Transactions on Medical Imaging.

[10]  Ahmed Hammouch,et al.  New method of tumor detection using K-means classifier and thresholding process , 2015 .

[11]  Mark W. Schmidt,et al.  Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines , 2005, CVBIA.

[12]  Lawrence O. Hall,et al.  Automatic segmentation of non-enhancing brain tumors in magnetic resonance images , 2001, Artif. Intell. Medicine.

[13]  Kai Li,et al.  Image Segmentation with Fuzzy Clustering Based on Generalized Entropy , 2014, J. Comput..

[14]  Guido Gerig,et al.  Level-set evolution with region competition: automatic 3-D segmentation of brain tumors , 2002, Object recognition supported by user interaction for service robots.

[15]  Guido Gerig,et al.  Automatic brain tumor segmentation by subject specific modification of atlas priors. , 2003, Academic radiology.

[16]  Pierrick Coupé,et al.  Bayesian Non-local Means Filter, Image Redundancy and Adaptive Dictionaries for Noise Removal , 2007, SSVM.

[17]  Yan Zhu,et al.  Computerized tumor boundary detection using a Hopfield neural network , 1997, IEEE Transactions on Medical Imaging.