A novel multimodality anatomical image fusion method based on contrast and structure extraction

Abstract

Image modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and so on, reflect various levels of details about objects of interest that help medical practitioners to examine patients' diseases from different perspectives. A single medical image, at times, may not be sufficient for making a critical decision; therefore, providing detailed information from a different perspective may help in making a better decision. Image fusion techniques play a vital role in this regard by combining important details from different medical images into a single, information enhanced image. In this article, we present a novel weighted term multimodality anatomical medical image fusion method. The proposed method, as a first step, eliminates the distortions from the source images and afterward, extracts two pieces of crucial information: the local contrast and the salient structure. Both the local contrast and salient structure are later combined to obtain the final weight map. The obtained weights are then passed through a fast guided filter to remove the discontinuities and noise.Lastly, the refined weight map is fused with source images using pyramid decomposition to get the final fused image. The proposed method is accessed and compared both qualitatively and quantitatively with state-of-the-art techniques. The result illustrates the performance superiority and efficiency of the proposed method.

Enhanced multimodal medical image fusion based on Pythagorean fuzzy set- An innovative approach

“…Various existing methods are included in this experiment such as PCA, DWT, Average (AVG) method, IFS 23 , SIFS 24 , GFF 39 , CSE 40 , and JBF-LGE 41 , which shows the superiority and efficiency of the proposed fusion method. In this paper, all comparative methods are utilized and meet the criterion and terms according to their publications.…”

Advancing multimodal medical image fusion: an adaptive image decomposition approach based on multilevel Guided filtering

“…The variations between organs and tissues in various images may be quite low, resulting in a poor visual effect when viewed through human eye. Because human eyes can only respond to brightness within a particular range [ 4 , 27 ]. Image enhancement is essential in the field of image processing because it optimizes image quality by highlighting useful information and eliminating redundant information [ 28 ].…”

“…Image fusion is a useful approach to improve computer vision processing since it seeks to combine details from several input images acquired by multimodal imaging equipment [7,8]. Therefore, throughout the process, fusion algorithms should provide strong contrast and integrate the necessary information without generating any artefacts [4]. The aim is to foster medical diagnosis decision-making so the image fusion process consists of a variety of methodologies and research fields, spanning from image processing methods, pattern recognition and computer vision [9][10][11][12][13][14].…”

“…In some cases, a radiologist needs to bring together details from different modalities without sacrificing the original image’s attributes [ 3 ]. Additionally, collecting different images from the same patient takes extra space, processing power and time [ 4 ]. Recently, as medical imaging continued to advance, multimodal medical image fusion improved remarkably.…”

CEFusion: Multi‐Modal medical image fusion via cross encoder

“…While these methods do not consider spatial orientation selectivity in the decomposition process, which leads to the edge of the fused image artifacts. (3) Based on salient features [39], such as CSE [40], a multi-modal anatomical medical image fusion method for extracting local contrast and prominent structure. However, these artificial extraction methods make it more difficult to design fusion rules.…”