Abstract:
: In order to better accurately forecast the diagnostic features, medical image fusion attempts to combine multi-focus and
multimodal medical data into an original image. The outcomes of deep learning-based image processing can be visually beautiful. The
incomprehensibility of the outcomes in Medical Imaging is a critical topic. This paper offers a feature-level multi-focus, multi-exposure,
and multimodal image fusion using a hybrid layer of Principal Component Analysis (PCA) and Guided Filter (GF) to maximize the
anatomical details and eliminate significant noise and artefacts. The proposed method utilizes a Convolutional Neural Network (CNN)
based network for feature extraction. The original image is initially decomposed using Principal Component Analysis (PCA). A PCA
decreases its dimensionality while preserving all of the essential information in the picture in the first stage and produces a revised
weight map. A Guided Filter is used at the PCA output to uphold the edges and further augment the features, reducing the ringing
and blurring effects. In the third step, a pre-trained CNN network creates a new weight map by extracting critical characteristics from
pictures in the input dataset. The output feature map combines the weight maps produced by the GF and CNN, which is further fused
with the reference picture to create the fused image output. The contribution of developed method:
• To improvise image quality features by removing noise, ringing and blurring.
• Increase the quality of the image by using the hybrid mechanism for extracting more underlying critical features of images [1].
The estimation is based on three multimodal imaging datasets, including CT-MRI, MRI-PET, and MRI-SPECT. Furthermore, the
proposed method excels in existing state-of-the-art techniques in terms of fusion quality. .
