A New Robust Adaptive Fusion Method for Double-Modality Medical Image PET/CT

Author:

Zhou Tao12ORCID,Lu Huiling3ORCID,Hu Fuyuan4ORCID,Shi Hongbin5,Qiu Shi6ORCID,Wang Huiqun7

Affiliation:

1. School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China

2. Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China

3. School of Science, Ningxia Medical University, Yinchuan 750004, China

4. School of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

5. Department of Urology, The General Hospital of Ningxia Medical University, Yinchuan 750004, China

6. Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

7. School of Public Health and Management, Ningxia Medical University, Yinchuan 750004, China

Abstract

A new robust adaptive fusion method for double-modality medical image PET/CT is proposed according to the Piella framework. The algorithm consists of the following three steps. Firstly, the registered PET and CT images are decomposed using the nonsubsampled contourlet transform (NSCT). Secondly, in order to highlight the lesions of the low-frequency image, low-frequency components are fused by pulse-coupled neural network (PCNN) that has a higher sensitivity to featured area with low intensities. With regard to high-frequency subbands, the Gauss random matrix is used for compression measurements, histogram distance between the every two corresponding subblocks of high coefficient is employed as match measure, and regional energy is used as activity measure. The fusion factor d is then calculated by using the match measure and the activity measure. The high-frequency measurement value is fused according to the fusion factor, and high-frequency fusion image is reconstructed by using the orthogonal matching pursuit algorithm of the high-frequency measurement after fusion. Thirdly, the final image is acquired through the NSCT inverse transformation of the low-frequency fusion image and the reconstructed high-frequency fusion image. To validate the proposed algorithm, four comparative experiments were performed: comparative experiment with other image fusion algorithms, comparison of different activity measures, different match measures, and PET/CT fusion results of lung cancer (20 groups). The experimental results showed that the proposed algorithm could better retain and show the lesion information, and is superior to other fusion algorithms based on both the subjective and objective evaluations.

Funder

North Minzu University

Publisher

Hindawi Limited

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine

Reference31 articles.

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3. Self-adaption fusion algorithm of lung cancer PET/CT based on Piella frame and DT-CWT;Z. Tao;Journal of University of Science and Technology of China,2017

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