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

Autor:	Sourabh Mukharjee, Minakshi Sharma
Rok vydání:	2013
Předmět:	Adaptive neuro fuzzy inference system Artificial neural network Computer science business.industry Feature extraction Scale-space segmentation Pattern recognition Feature selection Image segmentation Fuzzy logic Feature (computer vision) Computer vision Artificial intelligence business
Zdroj:	Advances in Computing and Information Technology ISBN: 9783642315510 ACITY (2)
DOI:	10.1007/978-3-642-31552-7_35
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3ecdd3cd740bf76c99813fdcfb28a847 https://doi.org/10.1007/978-3-642-31552-7_35 Zobrazit plný text záznamu