Abstract—Brain tumor is an abnormal mass of tissue with uncoordinated growth inside the skull which may invade and damage nerves and other healthy tissues. Non-homogeneities of the brain tissues result in inaccurate detection of tumor boundaries with the existing methods for contrast enhancement and segmentation of magnetic resonance images (MRI).This paper presents an improved framework for computer aided detection of brain tumor. This involves enhancement of cerebral MRI features by incorporating enhancement approaches of both the frequency and spatial domain. The proposed method requires de-noising in wavelet domain followed by enhancement using a non-linear enhancement function. Further an iterative enhancement algorithm is applied for enhancing the edges using the morphological filter. Segmentation of the brain tumor is finally obtained by employing large sized structuring elements along with thres holding. Simulation results along with the estimates of quality metrics portray significant improvement of contrast, enhancement of edges along with detection of boundaries in comparison to other recently developed methods.
Index Terms—brain tumor, daubechies wavelet, discrete wavelet transform, sigmoid function, magnetic resonance imaging, morphological filter, structuring element.
Abhishek Raj, Alankrita, and Akansha Srivastava are B. Tech. finalsemester students in Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow (U.P.), India (e-mail:email@example.com,firstname.lastname@example.org, email@example.com).
Vikrant Bhateja is Assistant Professor in Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow (U.P.), India (e-mail:firstname.lastname@example.org).
Cite: Abhishek Raj, Alankrita, Akansha Srivastava and Vikrant Bhateja, "Computer Aided Detection of Brain Tumor in Magnetic Resonance Images," International Journal of Engineering and Technology vol. 3, no. 5, pp. 523-532, 2011.