Brain tumor segmentation from MRI using fractional sobel mask and watershed transform

In this paper a novel brain tumor segmentation scheme using fractional order sobel mask and marker controlled watershed transform is proposed. To obtain the bright tumor region, regional maxima operation is performed on morphological preprocessed input T2-weighted MR image. The output regional maxima image is taken as an internal marker. Distance transform based watershed transform is applied on regional maxima image, the watershed ridge lines are used as external marker. Now, the fractional sobel mask of order a=0.3 is applied on input T2-weighted MR brain image to obtain gradient magnitude image. The segmentation of tumor region is achieved by using the watershed transform of gradient magnitude image with the help of derived internal and external markers. Region of interest (ROI) is selected to get the final segmented tumor image. Simulations are performed on images taken from the BRATS-2013 dataset for different values of a. For a = 0.3 values of accuracy, sensitivity and specificity performance parameters are comparable to other schemes compared. Moreover, fractional order a provides additional degree of freedom in optimizing the segmentation results. Proposed scheme can be used to segment other types of tumors and also for segmentation of CT images.

[1]  P. Kalavathi,et al.  SKULL STRIPPING OF MRI HEAD SCANS BASED ON CHAN-VESE ACTIVE CONTOUR MODEL , 2012 .

[2]  Ghazanfar Latif,et al.  Classification and segmentation of brain tumor using texture analysis , 2010 .

[3]  Cedric Nishan Canagarajah,et al.  Image segmentation using a texture gradient based watershed transform , 2003, IEEE Trans. Image Process..

[4]  Nooshin Nabizadeh,et al.  Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features , 2015, Comput. Electr. Eng..

[5]  Dan Tian,et al.  A fractional-order edge detection operator for medical image structure feature extraction , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[6]  Shohreh Kasaei,et al.  Benign and malignant breast tumors classification based on region growing and CNN segmentation , 2015, Expert Syst. Appl..

[7]  Jie Wu,et al.  Texture Feature based Automated Seeded Region Growing in Abdominal MRI Segmentation , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[8]  Atiq Islam,et al.  Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors , 2013, IEEE Transactions on Biomedical Engineering.

[9]  S. Bauer,et al.  Atlas-based segmentation of brain tumor images using a Markov Random Field-based tumor growth model and non-rigid registration , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[10]  Nitesh Sinha,et al.  A fully automated algorithm under modified FCM framework for improved brain MR image segmentation. , 2009, Magnetic resonance imaging.

[11]  Kumar Rajamani,et al.  Brain tumor extraction from MRI brain images using marker based watershed algorithm , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).