Zero-Learning Fast Medical Image Fusion

Fayez Lahoud, Sabine Süsstrunk
2019
Article de conférence

Clinical applications, such as image-guided surgery and noninvasive diagnosis, rely heavily on multi-modal images. Medical image fusion plays a central role by integrating information from multiple sources into a single, more understandable output. We propose a real-time image fusion method using pretrained neural networks to generate a single image containing features from multi-modal sources. The images are merged using a novel strategy based on deep feature maps extracted from a convolutional neural network. These feature maps are compared to generate fusion weights that drive the multi-modal image fusion process. Our method is not limited to the fusion of two images, it can be applied to any number of input sources. We validate the effectiveness of our proposed method on multiple medical fusion categories. The experimental results demonstrate that our technique achieves state-of-the-art performance in both visual quality, objective assessment, and runtime efficiency.

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Fayez Lahoud, Sabine Süsstrunk
2019
Article de conférence

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https://infoscience.epfl.ch/record/270048?ln=fr

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Zero-Learning Fast Medical Image Fusion

Fayez Lahoud, Sabine Süsstrunk

Clinical applications, such as image-guided surgery and noninvasive diagnosis, rely heavily on multi-modal images. Medical image fusion plays a central role by integrating information from multiple sources into a single, more understandable output. We propose a real-time image fusion method using pre-trained neural networks to generate a single image containing features from multi-modal sources. The images are merged using a novel strategy based on deep feature maps extracted from a convolutional neural network. These feature maps are compared to generate fusion weights that drive the multi-modal image fusion process. Our method is not limited to the fusion of two images, it can be applied to any number of input sources. We validate the effectiveness of our proposed method on multiple medical fusion categories. The experimental results demonstrate that our technique achieves state-of-the-art performance in both visual quality, objective assessment, and runtime efficiency.

International Society of Information Fusion

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Zero-Learning Fast Medical Image Fusion

Fayez Lahoud

Clinical applications, such as image-guided surgery and noninvasive diagnosis, rely heavily on multi-modal images. Medical image fusion plays a central role by integrating information from multiple sources into a single, more understandable output. We propose a real-time image fusion method using pre trained neural networks to generate a single image containing features from multi-modal sources. The images are merged using a novel strategy based on deep feature maps extracted from a convolutional neural network. These feature maps are compared to generate fusion weights that drive the multi-modal image fusion process. Our method is not limited to the fusion of two images, it can be applied to any number of input sources. We validate the effectiveness of our proposed method on multiple medical fusion categories. The experimental results demonstrate that our technique achieves state-of-the-art performance in both visual quality, objective assessment, and runtime efficiency.

IEEE2019

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Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

Hatef Otroshi Shahreza, Seyedeh Sahar Sadrizadeh

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performance of deep neural networks in different applications of image processing and computer vision, we present an end-to-end fully convolutional neural network to remove impulsive noise from images. To train our network, we generate a customized dataset with various noise densities in which the highly corrupted images are more frequent. Hence, our convolutional neural network is blind since the percentage of impulsive noise is not required as prior knowledge. Moreover, we define a multi-term loss function to train our network. In particular, we define a novel term to impose the sparsity nature of impulsive noise. Experimental results indicate that our deep learning approach significantly outperforms other state-of-the-art methods in terms of reconstruction quality and speed on a system equipped with GPU. Meanwhile, we introduce a fast iterative method, as a post-processing stage, to further improve the reconstruction quality of our neural network. The proposed post-processing algorithm improves the reconstruction quality in only a fraction of a second. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

ELSEVIER2022

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