Title:Fusion of Multimodal Medical Images based on Fine-grained Saliency and
Anisotropic Diffusion Filter
Volume: 20
Author(s): Harmanpreet Kaur*, Renu Vig, Naresh Kumar, Apoorav Sharma, Ayush Dogra and Bhawna Goyal
Affiliation:
- Department of Electronics and Communication Engineering, UIET, Panjab University, Chandigarh 160014, India
Keywords:
Biomedical image fusion, Healthy lives, Fine-grained saliency, Image quality assessment, Anisotropic diffusion filter, MRI.
Abstract:
Background:
A clinical medical image provides vital information about a person's health and bodily condition. Typically, doctors monitor and examine several
types of medical images individually to gather supplementary information for illness diagnosis and treatment. As it is arduous to analyze and
diagnose from a single image, multi-modality images have been shown to enhance the precision of diagnosis and evaluation of medical conditions.
Objective:
Several conventional image fusion techniques strengthen the consistency of the information by combining varied image observations; nevertheless,
the drawback of these techniques in retaining all crucial elements of the original images can have a negative impact on the accuracy of clinical
diagnoses. This research develops an improved image fusion technique based on fine-grained saliency and an anisotropic diffusion filter to
preserve structural and detailed information of the individual image.
Methods:
In contrast to prior efforts, the saliency method is not executed using a pyramidal decomposition, but rather an integral image on the original scale
is used to obtain features of superior quality. Furthermore, an anisotropic diffusion filter is utilized for the decomposition of the original source
images into a base layer and a detail layer. The proposed algorithm's performance is then contrasted to those of cutting-edge image fusion
algorithms.
Results:
The proposed approach cannot only cope with the fusion of medical images well, both subjectively and objectively, according to the results
obtained, but also has high computational efficiency.
Conclusion:
Furthermore, it provides a roadmap for the direction of future research.
