抽象的な

Intelligent Based Brain Tumor Detection Using ACO

Punithavathy Mohan,Vallikannu. AL, B.R.Shyamala Devi, B.C.Kavitha

Cancer is the uncontrolled growth of abnormal cells in the body. It can develop in almost any organ or tissue, such as the lung, colon, breast, skin, bones, or nerve tissue. Among these, brain cancer is one of the most threatening diseases and leading cause of cancer death in young people. Once diagnosed, patients have just a five percent chance of surviving this extremely aggressive disease. Hence detecting the brain tumor in early stage is very essential to improve the survival rate. Segmentation is an important aspect in medical image processing where, identification of abnormalities in brain is difficult. MRI (Magnetic Resonance Imaging) Scan analyses the soft tissues in human body, whereas CT (Computed Tomography) Scan is used for observing bone structures. MRI provides detailed information about brain tumor anatomy, cell structure and vascular supply, making it an important tool for the effective diagnosis, treatment and monitoring of the disease. This paper deals with Enhancement, Segmentation, Extraction and Classification of the MR Brain Image. The algorithm proposed here is CLAHE algorithm, Ant Colony Optimization (ACO) and K-means algorithm. ACO is used for segmentation of the image and K-means algorithm is used for classification of normal and abnormal tissues in the MR brain image with accuracy and reduced time complexity.