Automatic Brain Tumor Detection UsingK-Means and RFLICM

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering

article

A.J.Patil¹  Prerana Jain¹  Ashwini Pachpande¹ 

[1] Dept of Electronics and Telecommunication

关键词: Segmentation;    USM;    K-Means;    RFLICM;    Canny edge detection;    SCM classifier;   

DOI  :  10.15662/ijareeie.2014.0310071

来源: Research & Reviews

【 摘 要 】

In this paper presented a simple method for detection of area of tumor in brain MRI. The tumor is an uncontrolled growth of tissues in any part of the body. As it is known, the brain tumor is inherently serious and life threatening. Most of research in developed countries shows that the numbers of people who have brain tumors were died due to the fact of inaccurate detection. Tumors have different characteristics and different treatment. However the manual tumor detection method of detection takes more time for the determination size of tumor. To avoid that, in this paper presented computer aided method for detection of brain tumor based on the combination of two algorithms, Kmeans and improved fuzzy C-means (RFLICM) algorithm for image segmentation by introducing weighted fuzzy factor local similarity measure to make a trade-off between image detail and noise. This method allows the segmentation of tumor tissue with accuracy. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MR image and its position and the shape is determined.

【 授权许可】

Unknown