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Content introduction
In modern medical diagnosis, image fusion technology plays an important role in improving the quality and accuracy of medical images. Medical image fusion is the fusion of images from different modalities or different time points to provide more comprehensive and accurate information, thereby helping doctors make more accurate diagnosis and treatment plans. In this blog post, we will introduce a pixel fusion method based on Gaussian filter and PS search for the fusion of multi-modal medical images and multiple exposure images.
First, let us understand the principle of Gaussian filter. Gaussian filter is a linear smoothing filter commonly used for smoothing operations in image processing. It works by performing a convolution operation on the image, replacing the value of each pixel with the weighted average of its surrounding pixels. The weight value of the Gaussian filter is determined by a Gaussian function that has a maximum value near the center pixel and gradually decreases with increasing distance. This weight distribution enables the Gaussian filter to effectively remove noise from images while retaining image details.
Next, we will introduce the PS search algorithm. The PS search algorithm is an image fusion method based on pixel similarity, which determines the best match by comparing the pixel differences between different images. The algorithm first selects a reference image and then searches other images for patches of pixels that are similar to the reference image. Similarity measures are usually calculated using the Euclidean distance or correlation coefficient between pixels. Once the best match is found, blocks of pixels can be copied from the other images into the reference image, allowing the images to be fused.
Now, we combine Gaussian filter and PS search for the fusion of multi-modal medical images and multiple exposure images. First, we apply a Gaussian filter to each input image to remove noise and smooth the image. Then, we select one image as a reference image and use the PS search algorithm in other images to find pixel blocks that are similar to the reference image. By copying these blocks of pixels from other images into a reference image, we can fuse information from different images together, resulting in a more comprehensive and accurate image.
This pixel fusion method based on Gaussian filter and PS search has wide applications in medical image processing. It can be used for the fusion of multi-modal medical images, such as fusing MRI and CT images together to provide more comprehensive anatomical information. In addition, it can also be used for the fusion of multiple exposure images, such as fusing multiple exposure X-ray images together to improve image quality and reduce radiation dose.
To summarize, pixel fusion is an important image processing technology with wide applications in medical diagnosis. The pixel fusion method based on Gaussian filter and PS search can effectively fuse multi-modal medical images and multiple exposure images, thereby providing more comprehensive and accurate image information. With the continuous development of medical image processing technology, we believe that this fusion method will play a more important role in future medical diagnosis.
Part of the code
function [f]=mfiltw(images,k)</code><code>% Gaussian of differences: a simple and efficient general image fusion method</code><code>?</code><code>% Please cite this study as:</code><code>% Kurban, R. Gaussian of Differences: A Simple and Efficient General Image Fusion Method. Entropy 2023, 25, 1215. https://doi.org/10.3390/e25081215</code><code>?</code><code>kernelsize=2*k + 1;</code><code>sigma=(kernelsize-1)/6;</code><code>h=fspecial( 'gaussian',kernelsize,sigma);</code><code>?</code><code>[m,n,imagecount]=size(images);</code><code>cc=zeros(m, n,imagecount);</code><code>for i=1:imagecount</code><code> [ir,ic]=edges_func(padarray(double(images(:,:,i)),[k k] ,'symmetric'));</code><code> cc_temp=conv2(sqrt(ir + ic), h,'valid');</code><code> cc(:,:,i)=cc_temp + eps; </code><code>end</code><code>?</code><code>lowlim=(1/imagecount)*0.1;</code><code>uplim=1-lowlim;</code> code><code>?</code><code>sumcc=sum(cc,3);</code><code>f=zeros(m,n);</code><code>for i=1: imagecount</code><code> fw=cc(:,:,i)./sumcc;</code><code> fw(fw<lowlim)=lowlim;</code><code> fw(fw>uplim )=uplim;</code><code> fws(:,:,i)=fw;</code><code> f = f + fw.*double(images(:,:,i));</code> code><code>end</code><code>?</code><code>?</code><code>function [yr,yc]=edges_func(x)</code><code>yr=conv2 (x,[0 1 -1]','same').^2;</code><code>yc=conv2(x,[0 1 -1],'same').^2;</code><code>?
Operation results
References
Kurban, R. (2023). Gaussian of Differences: A Simple and Efficient General Image Fusion Method. Entropy, 25(8), 1215. https://doi.org/10.3390/e25081215