Brain Tumor Segmentation Using Genetic Algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS)

  • Minakshi Sharma
  • Sourabh Mukharjee
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


Medical image segmentation plays an important role in treatment planning, identifying tumors, tumor volume, patient follow up and computer guided surgery. There are various techniques for medical image segmentation. This paper presents a image segmentation technique for locating brain tumor (Astrocytoma-A type of brain tumor). Proposed work has been divided in two phases-In the first phase MRI image database (Astrocytoma grade I to IV) is collected and then preprocessing is done to improve quality of image. Secondphase includes three steps-Feature extraction, Feature selection and Image segmentation. For feature extraction proposed work uses GLCM (Grey Level co-occurrence matrix). To improve accuracy only a subset of feature is selected using Genetic algorithm and based on these features fuzzy rules and membership functions are defined for segmenting brain tumor from MRI images of .ANFIS is a adaptive network which combines benefits of both fuzzy and neural network. Finally, a comparative analysis is performed between ANFIS, neural network, Fuzzy, FCM, K-NN, DWT+SOM, DWT+PCA+KN, Texture combined +ANN, Texture Combined+ SVM in terms of sensitivity, specificity, accuracy.


ANFIS Brain tumor(Astrocytoma) sensitivity specificity accuracy MR images Neural network Fuzzy ANFIS FCM K-NN GLCM Genetic algorithm 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Minakshi Sharma
    • 1
  • Sourabh Mukharjee
    • 2
  1. 1.Department of ITGIMT KaniplaKurukshetraIndia
  2. 2.Department of Computer ScienceBanasthali UniversityJaipurIndia

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