Abstract
Medical imaging plays a significant role in the field of medical science. In present scenario image segmentation is used to extract abnormal tissues from normal tissues clearly in medical images. Tumor detection through brain MRI using automatic system is effective and consumes lesser time which helps doctor in diagnosis. A Tumor can convert into cancer, which is major leading cause of death. Automation of tumor detection is required for detecting tumor on early stage. Proposed work presents hybrid technique for information retrieval from brain MRI images. This research work presents an efficient technique based on K-means and artificial neural network (KMANN). GLCM (Grey Level co-occurrence matrix) used for feature extraction. Fuzzy Inference System is created using extracted feature which followed by thresholding, morphological operator and Watershed segmentation for brain tumor detection. Proposed method is used to identifying affected part of brain and size of tumor from MRI image with the help of MATLAB R2013b is used.
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Sharma, M., Purohit, G.N., Mukherjee, S. (2018). Information Retrieves from Brain MRI Images for Tumor Detection Using Hybrid Technique K-means and Artificial Neural Network (KMANN). In: Perez, G., Mishra, K., Tiwari, S., Trivedi, M. (eds) Networking Communication and Data Knowledge Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 4. Springer, Singapore. https://doi.org/10.1007/978-981-10-4600-1_14
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DOI: https://doi.org/10.1007/978-981-10-4600-1_14
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