Multi Modal Medical Image Fusion using Convolution Neural Network
| Autor: | Tripty Singh, Ravi Nayar, Shiv Kumar, Maneesha P |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Image fusion
Contextual image classification Pixel Matching (graph theory) Artificial neural network Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Convolutional neural network Computer vision Noise (video) Artificial intelligence business |
| Zdroj: | 2019 Third International Conference on Inventive Systems and Control (ICISC). |
| DOI: | 10.1109/icisc44355.2019.9036373 |
| Popis: | Medical image fusion have very important rolefor disease diagnosis and medical image analysis.An application to get complementary information from multiple images of different modalities. It is extensively used to combineinfor-mation from multiple images into single image with good accuracy. In our paper multimodal medical image fusion based on convolutional nueral network(CNN) is proposed. In this method a CNN model is created which will contain the pixel activity information of the input images. Image is decomposed into highly matching and low matching and separatefusion method is applied to both type of images. Beside this main important factor is to reduce noise because noise will affect the pixel intensities.so we will implement a new method to reduce noise in this manner. This method is to combine affected pixels of different images we are going to fuse. Different affected images will undergo an test for checking whether it is having noise or not. Then effected image will undergo a filtering algorithmtogetnoiselessimageforprovidingmoreclarity. |
| Databáze: | OpenAIRE |
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