Medical image fusion: A survey of the state of the art

Abstract

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Multimodal medical image fusion using Butterworth high pass filter and Cross bilateral filter

“…Most of the image fusion algorithms fall under pixel level. Pixel level is very commonly used in multisensor and multimodal image fusion [3,4]. Various morphological operators like opening, closing, erosion, dilation, and top hat transformation described in [5] are useful for detecting spatial relevant information.…”

“…It is the fusion of different modalities, to conglomerate the necessary information. Magnetic Resonance Imaging (MRI), Computerized Tomography (CT), Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) are different modalities used in multimodal medical image fusion [3]. Depending on the level of generalization, image fusion could be performed at pixel level, feature level and decision level [4].…”

Multimodal Medical Image Fusion Using Modified PCNN Based on Linking Strength Estimation by MSVD Transform

“…Medical imaging fusion (MIF) is the process that combines relevant information from two or more images acquired by one or more modalities and at different times [1]. MIF is more suitable and more informative for human visual perception than the input images.…”

“…Over the years, various MIF techniques have been proposed. MIF methods are summarized in different families such as the methods based on Wavelets, the morphological methods, the methods based on Fuzzy Logic or on Neural Network [1]. Each fusion method uses different techniques which are divided into a spatial domain (like Principal Component Analysis (PCA), Hue Intensity Saturation (HIS) and BroveyTransform) and a transform domain (like Discrete Wavelet Transform (DWT), Curvelet Transform (CT) , Nonsubsampled Contoured Transform (NSCT)) [2], [3].…”

Three-Dimensional Imaging With Multiple Degrees of Freedom Using Data Fusion

“…This ''requirement'' was generalized in [2] when considering that the data could be provided either by a single source (sensor) or by multiple sources. In the particular case of image fusion methodologies, an intense research effort is being made in fields like remote sensing [3]- [7], surveillance [8]- [11], and medicine [12]- [15], where the current tendency is in the use of a combination of sensors to optimize information acquisition and extraction. In the case of remote sensing, the use of multiple sensors may help in rural and urban planning [3], species classification [4], [5], and prevention of disasters [6], [7].…”