DocumentCode :
249030
Title :
Hidden Markov model-based multi-modal image fusion with efficient training
Author :
Shenoy, Renuka ; Shih, Min-Chi ; Rose, Kenneth
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, Santa Barbara, CA, USA
fYear :
2014
fDate :
27-30 Oct. 2014
Firstpage :
3582
Lastpage :
3586
Abstract :
Automated spatial alignment of images from different modalities is an important problem, particularly in bio-medical image analysis. We propose a novel probabilistic framework, based on a variant of the 2D hidden Markov model (2D HMM), to capture the deformation between multi-modal images. Smoothness is ensured via transition probabilities of the 2D HMM and cross-modality similarity via class-conditional, modality-specific emission probabilities. The method is derived for general multi-modal settings, and its performance is demonstrated for an application in cellular microscopy. We also present an efficient algorithm for parameter estimation. Experiments on synthetic and real biological data show improvement over state-of-the-art multi-modal image fusion techniques.
Keywords :
hidden Markov models; image fusion; image registration; learning (artificial intelligence); medical image processing; microscopy; 2D hidden Markov model; automated spatial alignment; biomedical image analysis; cellular microscopy; class condition; efficient training; modality specific emission probabilities; multimodal image deformation; multimodal image fusion; probabilistic framework; transition probability; Biomedical imaging; Hidden Markov models; Image segmentation; Mutual information; Training; Vectors; Viterbi algorithm; Biological image analysis; deformable; fusion; multi-modal; registration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
Type :
conf
DOI :
10.1109/ICIP.2014.7025727
Filename :
7025727
Link To Document :
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