International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
MULTIPLE ANALYSIS OF BRAIN TUMOR DETECTION BASED ON FCM 1Parul
Parmar, 2Vinay Thakur
1 M.Tech
(ECE), SSU Palampur, Himachal Pradesh, India. Professor (ECE), SSU Palampur, Himachal Pradesh, India. ---------------------------------------------------------------------***--------------------------------------------------------------------2 Assistant
Abstract - A Tumor is an abnormal mass of a tissue which
can be solid or fluid filled in any part of the body. The tumors are of different types and have different treatment. In this paper it is used to detect the brain tumor from MR images based on fuzzy rules. The Brain images can be seen by the MRI scan or CT scan. It is important to find out the tumor from MRI images but it is time consuming although, the MRI scan is more comfortable than any other scans for diagnosis. It do not affect the human body, as it is centered on the magnetic field and radio waves. There are various types of algorithm for brain tumor detection. But they may have some drawback in detection and extraction. According to the previous approaches brain tumor detection is done by preprocessing which is first step and then segmentation is done by fuzzy c-means algorithms the brain tumor is detected and it is identified. Comparing with the other algorithms the performance of fuzzy c-means plays a major role. Key Words: Tumor, MRI Scan, Preprocessing, Segmentation, Fuzzy c-means.
1. INTRODUCTION Doctors use many tests to find, or diagnose a brain tumor and learn the type of a brain tumor. They also do the tests to learn if it has spread to another part of body from where it started, doctors may also do tests to learn which treatment could work best For most of the types of tumor, taking the sample of the possible tumor is the only sure way for the doctor to know whether the area of the body has a tumor or not. Most brain tumors are not diagnosed until after symptoms appear. Often a brain tumor is initially diagnosed by an internist or a neurologist. An internist is a doctor who specializes in treating adults. A neurologist is a doctor who specializes in problems with the brain and central nervous system. In general, diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). Once MRI shows that there is a tumor in the brain, the most common way to determine the type of brain tumor. TUMOR The tumor are generally of two types i.e. primary or secondary. The primary brain tumor do not spread to another body parts and can be malignant or benign and secondary tumors if the part of the tumor spreads to another place and grows on its own, then it is known as secondary. Š 2017, IRJET
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The secondary brain tumors are always malignant. The malignant tumor is more dangerous and life threatening than benign tumor whereas malignant means the cells which are cancerous and benign means which are noncancerous. The main aim of this system is to make a system for detecting and identifying the tumor from MRI For detection and segmentation of brain tumor is that if we obtained the three dimensional image of brain tumor then we can also find out its tumor size and also can evaluate its tumor type. MAGNETIC RESONANCE IMAGING The MRI uses a large magnet and radio waves to look at organs and structures inside your body. Health care professionals use MRI scans to diagnose a variety of conditions. MRI are very useful for examining the brain and spinal cord . the MRI having better qualities then other medical imaging techniques. In this a special dye is used to enhance the the likelihood of detecting the brain tumor.
2 PROPOSED SYSTEM The proposed system it consists of four types named as Preprocessing, segmentation, segmentation using fuzzy cmeans, Feature extraction, According to the need the preprocessing step converts the image. It performs filtering of noise and other artifacts in the image and sharpening the edges in the image. RGB to gray conversion. The feature extraction is extracting the cluster which is based upon entropy using fuzzy rules which shows the predicted tumor at the FCM (Fuzzy C-means) output. The extracted cluster is given to the process. It applies a binary mask over the entire image. In the approximate reasoning step the tumor area is calculated using the method making the dark pixel darker and white brighter. In the approximate reasoning step the tumor area is calculated using the binarization method. That is the image having only two values either black or white (0 or 1). Here 256x256 JPEG image is a maximum image size. The binary image can be represented as a summation of total number of white and black pixels. The Pre-processing is done by filtering. Segmentation which is done by Fuzzy Cmeans algorithm. The feature extraction is done by fuzzy rules and finally, approximating the method to recognize the tumor size in MRI image using FCM detection method.
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