Multimodal medical image fusion based on discrete Tchebichef moments and pulse coupled neural network

Abstract

Multimodal medical image fusion plays a vital role in clinical diagnoses and treatment planning. In many image fusion methods-based pulse coupled neural network (PCNN), normalized coefficients are used to motivate the PCNN, and this makes the fused image blur, detail loss, and decreases contrast. Moreover, they are limited in dealing with medical images with different modalities. In this article, we present a new multimodal medical image fusion method based on discrete Tchebichef moments and pulse coupled neural network to overcome the aforementioned problems. First, medical images are divided into equal-size blocks and the Tchebichef moments are calculated to characterize image shape, and energy of blocks is computed as the sum of squared non-DC moment values. Then to retain edges and textures, the energy of Tchebichef moments for blocks is introduced to motivate the PCNN with adaptive linking strength. Finally, large firing times are selected as coefficients of the fused image. Experimental results show that the proposed scheme outperforms state-of-theart methods and it is more effective in processing medical images with different modalities.

Enhanced brain image retrieval using carrier frequency offset compensated orthogonal frequency division multiplexing for telemedicine applications

“…Image fusion is the process of perceptually combining the important features from more than one image into a single image which retains the significant features of both the images. 4 The fusion process can be performed at different levels such as pixel, signal, and feature. The limitations of spatial domain methods are spectral distortion and contrast level reduction.…”

IGM‐based perceptual multimodal medical image fusion using free energy motivated adaptive PCNN

“…There are various medical images such as computed tomography (CT), magnetic resonance imaging (MRI), single photon emission CT (SPECT), and positron emission CT (PET). Single modality medical images may not enough to provide clinical needs to radiologists . Multimodal medical image fusion provides a promising solution approach by integrating information of different modality images into a visual enhanced fused image.…”

“…However, its high computational complexity hinders the performance of medical image fusion . Furthermore, it will cause blurring effect at the edge . Recently, researches on brain theory and neuroscience, the Bayesian brain theory, and the free energy (FE) principle reported that the brain works with an internal generative mechanism (IGM) for visual perception and understanding.…”

“…Single modality medical images may not enough to provide clinical needs to radiologists. 1,2 Multimodal medical image fusion provides a promising solution approach by integrating information of different modality images into a visual enhanced fused image. For example, by fusion of CT with MRI images, it is possible to provide dense structures like bones and implants with less distortion correlation to the normal and pathological soft tissues information.…”

“…14 Furthermore, it will cause blurring effect at the edge. 1 Recently, researches on brain theory and neuroscience, the Bayesian brain theory, 15 and the free energy (FE) principle 16,17 reported that the brain works with an internal generative mechanism (IGM) for visual perception and understanding. Motivated by IGM theory and FE principle, we present a perceptual multimodal medical image fusion method with FE motivated adaptive PCNN by employing IGM.…”