Journal of Basic and Applied Engineering Research Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 1, Number 6; October, 2014 pp. 22-26 © Krishi Sanskriti Publications http://www.krishisanskriti.org/jbaer.html Noise Removal from Brain Image using Region Filling Technique Daizy Deb 1 , Sudipta Roy 2 1,2 Dept. of Information Technology, Assam University, Silchar, Assam, India Abstract: In today’s world, one of the reason in rise of mortality among the people is brain cancer. Brain tumour is the main cause of brain cancer. A tumour can be defined as any mass caused by abnormal or uncontrolled growth of cells. This mass of tumour grows within the skull, due to which normal brain activity is hampered. Which is if not detected in earlier stage, can take away the person’s life. Hence, it is very important to detect the brain tumour as early as possible. For detection of brain tumour, first we have to read the MRI image of brain and then we can apply segmentation on the image. But in the MRI brain image, some confidential information of patient’s is always there. To apply segmentation, this unnecessary information has to be removed, as it can be considered as noise. Here we present an efficient method for removing noise from the MRI image of brain using Region filling method. Keywords: Brain tumor, Noise, Filtering, Region of Interest, Region Filling. 1. INTRODUCTION Brain cancer is one of the leading causes of death in the world now days. An uncontrolled growth of cancer cells in the brain leads to brain cancer, which is a very serious type of malignancy. A malignant brain tumour is the main cause of brain cancer. All brain tumours are not malignant, some are benign also. Brain cancer is also called glioma and meningioma [1]. According to the National Brain Tumour Society, US, over 600,000 people are living with the primary brain tumour. Among these 600,000 people, 28,000 are children under the age of 20. Metastatic brain tumours (cancer that spreads from other parts of the body to the brain) are the most common type of brain tumour, which is the reason of cancer for 20% to 40% of persons. Over 7% of all the primary brain tumours reported in the United States are diagnosed among children under the age of 20. 210,000 people in the United States are diagnosed with a primary or metastatic brain tumour every year i.e. over 575 people a day. In general, the risk of developing a malignant CNS or brain tumour over the course of one’s lifetime is less than 1%. But the risk increases with the age. 4.5 per 100,000 persons under the age of 20 will be diagnosed with a malignant brain tumour. After the age of 75, this rate rises to 57 per 100,000 persons. Among the people over the age of85, the risk stops increasing. The risk for developing brain cancer is very high among the people with a family history of brain cancer and those who had radiation therapy of the head. 2. RELATED WORKS IN NOISE REMOVAL T. Logeswari and M. Karnan [1] applied weighted median filter for removing the noise presented in the MRI image of the brain. Weighted median filter is a type of nonlinear filters. It retains the robustness and edge preserving capacity of the image. Dr. Samir Kumar Bandyopadhyay [3] removed noise based on Maximum Difference Threshold value, which is constant threshold value determined by observation. Pratibha Sharma and co-authors [4] applied spatial noise filter for removing noise from the MRI image of a brain. Sudipta Roy, Samir K. Bandyopadhyay [5] first used high pass filter and then finally used median filter for removing noise. Here a high pass filter is used in matlab, by which each pixel of the image is replaced by weighted average of the surrounding pixels. Then merging of gray scale image and filtered image is done for enhancing the image quality. Median filter is applied to the enhanced image. High pass filter is used by Rajesh C. Patil, Dr. A. S. Bhalchandra [6] for removing noise and then they applied median filter to enhance the quality of the image. Noise removal has to be done in such a way that it should not affect the portion of the brain in the image as each portion is the most important part to detect the tumor. Hence noise removal should not blur the image. 3. NOISE AND MEDICAL IMAGE “Noise” originally means “unwanted signal” i.e. noise represents unwanted information which deteriorates image quality. The process which affects the acquired image and is not part of the scene can be defined as noise. A random variation of brightness or color information in images can also be termed as noise [10].