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Effective Fusion Technique Using FCM Based Segmentation Approach to Analyze Alzheimer’s Disease
S. Mukherjee,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 178
Pages: 91 - 107
The integration of complementary information from multimodal images is called fusion. In this study an efficient fusion technique is proposed to extract salient features from the segmented images of human brain that helps to study the prognosis of Alzheimer’s disease. The significant information of each RGB component of low resolution functional PET image has been picked up by fuzzy clustering technique using appropriate membership functions. Intelligent choice of membership functions captures the salient features and spatial structures of the investigated region and does not incorporate any artifacts. To integrate each RGB component of the segmented PET image with MRI is done using principal component analysis approach as there is a possibility of losing relevant information in the conventional simple averaging process. Principal component approach is applied for weighted averaging scheme which is capable to extract important features of each color plane of PET with MRI. The experimental results show that the fused images are the successful combination of anatomical and functional information. © 2020, Springer Nature Switzerland AG.
About the journal
JournalData powered by TypesetIntelligent Systems Reference Library
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH