Clustering based Multi-modality Medical Image Fusion

Abstract

The unwanted data obtained through the medical image fusion is the main problem in biomedical applications, guided-image surgical and radiology. The Stationary Wavelet Transform (SWT) denoted the various advantages over conventional representation of imaging approach. In this research article we introduced innovative multi-modality fusion technique for medical image fusion based upon the SWT. In our approach it disintegrates of source images into approximation layers (coarse layer) and detail layers through the Stationary Wavelet Transform scheme, then applying of the Fuzzy Local Information C-means Clustering (FLICM) and Local contrast fusion approach to overcome the blurring effect, sensitiveness and conserve the quality evaluation in the distinguish layers. The demonstration shows that it preserves more detailed information in the source images and it enhances the more quality features and edge preserved of the final fused image obtained through the reconstruction procedure by recursive initial steps. The different methodologies with other techniques to evaluate performance such as mutual information, edge based similarity and blind image quality. This shows that in both the objective and subjective analysis our methodology results attained more supercilious performance.

Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform

“…Dhaka et al [ 39 ] analyzed the differences between the stationary wavelet transform (SWT) and the discrete wavelet transform (DWT) for different applications and found SWT outperforms DWT. According to a study by Dhaundiyal [ 40 ], a novel SWT-based multimodality fusion approach was presented for medical image fusion. In this method, the source images are first decomposed into an approximation layer (coarse layer) and a detail layer using the SWT scheme and then the Fuzzy Local Information C-Means Clustering (FLICM) and local contrast fusion approach are applied to the distinct layers to counteract the blurring effect, maintain sensitivity, and preserve quality evaluation.…”

Transformer-based framework for multi-class segmentation of skin cancer from histopathology images

“…Advancements in healthcare are multifaceted. Post-processing of CT images for improved image quality and reduced radiation dose ( 8 ), denoising of ECG signals for enhanced signal clarity ( 9 ), clustering-based multi-modality image fusion to minimize noise ( 10 ), and advanced diagnosis and detection of lung diseases ( 11 ) are some of the transformative contributions of AI and digital processing techniques in medical care. These innovations collectively mark significant progress in improving healthcare outcomes.…”