In past decades, image fusion has matured significantly in the application fields such as medical [1], military ^[2][3][2,3], and remote sensing [4]. Image fusion is a prominent application in the medical field for better analysis of human organs and tissues. In general, the medical image data is available from various imaging techniques such as magnetic resonance imaging (MRI), magnetic resonance angiography (MRA), computed tomography (CT), T1-weighted MR, T2-weighted MR, positron emission tomography (PET), and single-photon emission computed tomography (SPECT) [5]. Each technique has different characteristics.

Multimodal medical images are widely characterized into two types: anatomical and functional modalities, respectively. Anatomical modalities are, namely, MRI, MRA, T1-weighted MR, T2-weighted MR, and CT. CT images represent a clear bone structure with lower distortion but do not distinguish physical changes, while MRI images provide delicate tissue information with high spatial resolution. CT imaging is used to diagnose diseases such as muscle disease, vascular conditions, bone fractures and tumors etc. MRI imaging is used to diagnose various issues in medial regions such as brain tumors, multiple sclerosis, lung cancer and treatment, brain hemorrhage, and dementia etc. Magnetic resonance angiography, or MRA, is a subset of MRI that utilizes magnetic fields and radio waves, which create images of the body’s arteries, helping clinicians to detect blood flow abnormalities. The weighted MR-T1 images reveal fat, while weighted MR-T2 images provide water content.

Functional modalities are PET, and SPECT. PET imaging gives functionality of human organs with high sensitivity. The PET imaging technology is used to diagnosis different diseases such as Alzheimer’s disease, Parkinson’s disease, cerebrovascular accident, and hematoma. The other application areas of PET imaging are lung and breast cancer diagnosis, and cancer treatment.

SPECT imaging provides blood flow information with minimal spatial resolution, and is used for different diagnoses, namely, brain and bone disorders, and heart problems. The application areas in SPECT imaging are pelvis irradiation detection and treatment, vulvar cancer, breast cancer assessment, and head and neck cancer diagnosis ^[6][7][6,7]. However, single medical image data cannot provide the required information for diagnosis. To overcome this, multimodal medical image fusion is necessary.

Multimodal medical image fusion is the process of merging different modalities of medical images into a single output image. Its advantages include decreased uncertainty, resilient system performance, and higher reliability, all of which contribute to more accurate diagnosis, thus improving treatment. From the literature, authors have reported various multimodality combinations. Fusion of T1- and T2-weighted MR images produce a fused image, and is used to identify tumor regions [8]. The soft and hard tissue information from MRI and CT images, respectively, are combined into a single resultant image by fusion resulting in better image analysis [9]. The T1-weighted MR and MRA [10] combination provides perfect lesion locations with delicate tissues. The MRI–PET [11] combination and MRI–SPECT [12] combinations provide anatomical and functional information in a single image, which is used to better diagnosis disease and medical-related problems. The objective of this research article is to examine the relevance and advancement of information fusion approaches in medical imaging for investigation of clinical aspects and better treatment.

In any fusion strategy, two important requirements should be satisfied: it should not add any artifacts or blocking effects to the resultant image; and no information should be lost throughout the fusion process.

Image fusion techniques are broadly classified into three levels [13], namely, pixel-level, feature-level, and decision-level. In pixel-level fusion, image pixel values are directly merged. In feature level fusion, various salient features are involved in the fusion process such as texture and shape. In decision-level fusion, the input images are fused based on multiple algorithms with decision rules.

The preeminent research issue in medical image processing is to obtain maximum content of information by combining various modalities of medical images. Various existing techniques are included in this literature such as the simple average (Avg), maximum, and minimum methods. The average method provides a fused image with low contrast, while the maximum and minimum methods provide the less enhanced fused images. The Brovey method [14] gives color distortions. Hybrid fusion methods such as the intensity-hue saturation (IHS) and principal component analysis (PCA) [15] combination provides a degraded fused image with spatial distortions. However, the pyramid decomposition-based method [16] shows better spectral information, but the required edge information is not sufficient. Discrete cosine transform (DCT) [17] and singular value decomposition (SVD) [18] methods give a fused image, which has a more complementary nature but does not show clear boundaries of the tumor region. The multi-resolution techniques, such as discrete wavelet transform (DWT) [19], provides better localization in time and frequency domains, but cannot give the shift-invariance due to down-sampling. To overcome this the redundant wavelet transform (RWT) [20] was employed. However, the above technique is highly complex and cannot provide sufficient edge information. The contourlet transform (CONT) technique [21] provides more edge information in a fused image but does not provide the shift invariance. Shift invariance is the most desirable property and is applied in various applications of image processing. These are: image watermarking [22], image enhancement [23], image fusion [24], and image deblurring [25]. The above mentioned drawbacks are addressed by the non-subsampled contourlet transform (NSCT) [26] and non-subsampled Shearlet transform (NSST) ^[27][28][27,28]. Hybrid combinations of fusion techniques such as DWT and fuzzy logic [29] provide a fused image with low contrast because of the higher uncertainties and vagueness, which is present in a fused image. In general, medical images have poor illumination which means low contrast and poor visibility in some parts, which indicates uncertainties and vagueness. Visibility and enhancement are the required criteria in the medical field to diagnose the disease accurately. In the literature, various image enhancement techniques are reported, namely, gray-level transformation [30] and histogram-based methods [31]. Yet, these methods are not properly improving the quality of medical images. Zadeh [32] proposed a mathematical approach, namely, a fuzzy set in 1965. This fuzzy set approach has played a significant role by removing the vagueness present in the image. However, it did not eliminate the uncertainties. A fuzzy set does not provide reasonable results regarding more uncertainties because it considers only one uncertainty. This uncertainty is in the form of membership function, that lies between the range 0 to 1, where zero indicates the false membership function, and one indicates the true membership function. In the year 1986 Atanassov [33] proposed a generalized version of the fuzzy set i.e., intuitionistic fuzzy set (IFS), which handles more uncertainties in the form of three degrees. These degrees are membership, non-membership, and hesitation degrees. The IFS technique is highly precise, and flexible in order to handle uncertainties and ambiguity problems. TIn this literature review, the research gaps and drawbacks of various medical image fusion techniques are discussed and listed in Table 1: The main contribution of this research article is described as follows:

• A novel intuitionistic fuzzy set is used for the fusion process, which can enhance the fused image quality and complete the fusion process successfully.
• The intuitionistic fuzzy images are created by using the optimum value, α, which can be obtained from intuitionistic fuzzy entropy.
• The Intuitionistic cross-correlation function is employed to measure the correlation between intuitionistic fuzzy images and then produce a fused image without uncertainty and vagueness.
• The proposed fusion algorithm proves that the fused image has good contrast and enhanced edges and is superior to other existing methods both visually and quantitatively.