Fusion of Functional and Anatomical Images Based on Cartoon-Texture Analysis and Pulse-Coupled Neural Network

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Zamanzadeh Rojin; Shahdoosti Hamid Reza

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Journal Paper

Paper Information

Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2025 | Volume:16 | Issue:1 Page(s): 41-58

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Information Journal Paper

Title

Fusion of Functional and Anatomical Images Based on Cartoon-Texture Analysis and Pulse-Coupled Neural Network

Author(s)

Zamanzadeh Rojin | Shahdoosti Hamid Reza | Issue Writer Certificate

Keywords

Medical Image Fusion

Pulse-Coupled Neural Network

Cartoon-Texture Decomposition

Magnetic Resonance Imaging

Abstract

MRI and PET (or SPECT) image fusion enable the simultaneous visualization of anatomical details provided by the MRI image and functional details provided by the PET (or SPECT) image. This paper introduces a new image fusion framework for MRI and PET (or SPECT) medical images, which utilizes the features obtained through cartoon-texture decomposition in a pulse-coupled neural network. Firstly, the medical images are decomposed into cartoon and texture components such that the cartoon components represent piecewise smooth parts of the images with clear boundaries, and the texture components represent details of the images, such as edges and textured areas. Secondly, the cartoon components are fused by comparing the regional energy of coefficients, while the texture components are fused by feeding EOG (energy of image gradient) of coefficients into the pulse-coupled neural network to motivate the neurons. Experiments using several pairs of brain images show a state-of-the-art quality of the fused image, in terms of both visual appearance and objective criteria. Thanks to the efficient decomposition, the proposed method produces clear edges and details and does not suffer from color distortion.

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