An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

1  Introduction

Medical imagining modalities are used to create images of body parts for diagnostic and therapeutic purposes within digital health systems. Each type of imaging modalities gives different details about the different body regions to study or treat. There are many types of these techniques, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), X-ray, and Positron Emission Tomography (PET) [1].

CT uses X- rays and computers to create an image with more details to aid specialists in detecting several diseases and conditions. It is painless, rapid, and accurate. The CT images created during a CT scan can be reformatted at many levels. It can produce 3-dimensional images. CT images provide more details than X-rays, especially in soft tissues, and blood vessels. It was developed in 1970, and it is used till now. A CT scan can detect the size, shape, and location of structures found deep in the body, such as organs, tissues, or tumors [2].

MRI is a type of imagining technology that is used to generate cross-sectional images of the parts of the human. It uses a magnetic field and computer to create images with high resolution that is used to clarify the pathological changes of the human parts. It generates cross-sectional images of the human parts. It produces three-dimensional anatomical images in several planes [3].

Image fusion is the process that is used to combine multiple images to get an image with rich details. Medical image fusion techniques perform better in gathering several details from different imaging technologies, which may aid in the correct diagnosis of several diseases. The aim of the image fusion is to reduce the redundancy in the output while increasing relevant information to an application or task [4].

Deep learning is a kind of machine learning techniques [5]. Deep learning is implemented in several applications such as medical image analysis and achieves high performance. The CNN is a special type of deep learning applied for classification. It is decomposed of an input layer, convolutional layer, pooling layer, fully connected layer, and a classification layer. It comprises a feature extraction and classification network [6–11].

This paper introduces a deep learning technique for the medical image fusion process. This technique is based on CNN. The basic idea of the proposed technique is to extract features from CT and MRI images. Then, an additional process is carried out on the extracted features. After that, the fused feature map is reconstructed to obtain the fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as HM, HE, fuzzy technique, fuzzy type Π, and CLAHE. The performance of the proposed approach is evaluated by using various fusion metrics. The rest of this paper is arranged as follows. Section 2 clarifies the related work. Section 3 explains the proposed model of medical image fusion. Section 4 illustrates the evaluation of image quality. Simulation results and discussions are presented in Section 5. Finally, the conclusion is introduced in Section 6.

2  Related Work

Rajalingam et al. [12] presented a technique for medical image fusion. This technique consists of a Non-subsampled Contourlet Transform (NSCT) and Dual-Tree Complex Wavelet Transform (DTCWT). This technique was applied to fuse PET and MRI for the fusion process. The dimensions of the input image are 256 × 256. Two-level transformations were applied for the fusion process.

Liu et al. [13] proposed an attempt for medical image fusion depending on the image decomposition model and Nonsubsampled Shearlet Transform (NSST). It is performed to decompose the reference image into texture components and approximation components. A maximum fusion rule was performed to merge texture components to move salient gradient information to the fused image. Finally, a component synthesis process is implemented to generate the resulting image.

Li et al. [14] presented a model for medical image fusion depending on low-rank sparse component decomposition and dictionary learning. The source image was split into low-rank and sparse components to eliminate the noise and keep the textural details.

Polinati et al. [15] introduced a model for medical image fusion by applying empirical wavelet decomposition and Local Energy Maxima (LEM). This model is applied to integrate several imaging modalities such as MR, PET, and SPECT. In addition, Empirical Wavelet Transform (EWT) and LEM are applied to decrease the distortion of the images.

Chen et al. [16] presented a technique for medical image fusion depending on rolling guidance filtering. Firstly, the rolling guidance filter was used to split the reference medical images into structural and detail components. Finally, a sum modified Laplacian is used to extract the component details. The fused structural and detail components are obtained.

Nair et al. [17] presented a technique for medical image fusion based on NSST. Firstly, the pre-processing step was applied, such as Gaussian filtering, edge sharpening, and resizing. And then optimal registration was performed. After that, the Denoised Optimum B-Spline Shearlet Image Fusion (DOBSIF) was applied for image fusion. Finally, the segmentation process was implemented to the fused image to detect the tumor part.

Faragallah et al. [18] introduced a method for medical image fusion. This method starts with image registration and performing the histogram matching to decrease the artifacts of the fusion. After that, the NSST is applied for the fusion process, and the Modified Central Force Optimization (MCFO) is applied. Finally, the fused image quality is enhanced by an enhancement operation.

El-Shafai [19] introduced a medical image fusion and segmentation technique. This technique used the fusion and segmentation methods. Several research studies have worked on medical image fusion from several perspectives, as in [20–25].

3  Proposed Medical Image Deep Fusion-Based CNN Approach

The purpose of the fusion process is to obtain a certain image including sufficient information in order to help doctors and technicians to diagnose the diseases accurately. This paper presents a model for medical image fusion. This model performs CT and MRI image fusion. The first stage is the registration stage. The importance of registration is to ensure that each pixel in both input images is located in the same coordinates. The registration process is based on Gaussian filtering and key points registering. The second stage is the fusion stage. The fusion stage is based on a deep learning approach.

A sequence of the CNNs is applied to the input images in order to extract features from both registered input images. The resulting features of the input CT and MRI images are added to fuse them. After that, the fused feature map is reconstructed to obtain the fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as HM, HE, fuzzy technique, fuzzy type Π, and CLAHE. An evaluation process is executed on the fused image to make sure it contains high entropy compared to the input images. There are other evaluation metrics to evaluate the fused image, and the enhanced image will be discussed and investigated in the simulation results and discussion section. Fig. 1 shows the main steps of the proposed medical image fusion model.

Figure 1: Steps of the proposed medical image fusion model

3.1 Registration Process

The feature vector is extracted from the image; firstly, the image is convolved with many Gaussian kernels with several scales [26–28]:

L(i,j,σ)=G(i,j,σ)∗I(i,j)(1)

G(i,j,σ) is the Gaussian kernel with scale σ, and I(i,j) is the source image. The Gaussian kernel is formulated as:

G(i,j,σ)=12πσ2e−(i2+j2)/2σ2(2)

The Laplacian operator is performed to the various scales. Simply, the Difference of Gaussian (DoG) is applied as an alternative plan as clarified in Fig. 2 for the keypoints detection. The pixels at similar coordinates are compared to choose the points of the features across scales. The maximum points are chosen across the scales for extra feature extraction operations. Finally, the Laplacian is eliminated for simplicity. So, the DoG is used instead of the LoG. Fig. 2 shows the steps of applying the DoG process.

Figure 2: Steps of the DoG process

After choosing the key point, the gradient magnitude and phase are defined at this key point:

m(i,j)=(L(i+1,j)−L(i−1,j))2+(L(i,j+1)−L(i,j−1))2(3)

θ(i,j)=tan−1⁡(L(i,j+1)−L(i,j−1)L(i+1,j)−L(i−1,j))(4)

where L(i,j) is the gradient at (i,j). We choose a window of size 16 × 16 for every keypoint, as shown in Fig. 3. For each sub-window, the histogram angle of gradients is detected with four bins, where each bin is 45°. The feature vector of 128 points represents each feature point.

Figure 3: Key-point description

For the registration process, the MR image is considered as a source image in which the feature vectors and the feature points are extracted. Also, for the CT image, the feature vectors and feature points are extracted and compared with the MR image features. The process of matching depends on the minimum distance.

3.2 Deep Learning-Based Image Fusion

The fusion process is implemented between CT and MR images to get an image that includes more information than both input images. The fusion process is based on a deep learning process depending on CNNs. For the input image, the convolutional layer includes filters that are applied in a two-dimensional (2D) convolution operation. The number of resulting features is equal to the number of filters. This concept is very well suitable for the MR image as we can notice the minor changes in the image local activity levels. Fig. 4 clarifies an example of the process that occurs in the convolutional layer.

Figure 4: The process of convolutional neural network (CNN)

This paper introduces an image fusion approach based on CNNs. CNN achieves the optimum parameters of the model based on an optimization process for a loss function to expect an input as near as possible to the desired target. The input images are registered in order to achieve an optimum fused image with sufficient information rather than the input images. The fusion operation O(I) is modeled with a feed-forward propagation FW(I), where F points to the network structure and W points to the weights learned by the optimization process.

Traditional loss functions such as the square error (SE) cannot be performed efficiently for the purpose of fusion optimization. So, O(I) is absent for this purpose. This paper adopts Fusion Structural Similarity Index Metric (FSSIM) as a loss function for the fusion optimization process [29]. It evaluates the structural integrity loss in addition to the luminance consistency in many scales.

Fig. 5 shows an overall detailed model of the proposed medical image fusion-based CNN model [29]. The series of convolutional layers (CNVs) are connected consecutively. The image pairs in a three-dimension (3D) representation will be the input for this architecture. Where the fusion occurs in the domain of the pixel itself, the ability of the feature learning is not included in this kind of CNNs architecture. The proposed architecture consists of reconstruction layers, a fusion layer, and feature extraction layers. Fig. 6 shows the stages of the image fusion model based on CNN.

Figure 5: The in-detail steps of the proposed image fusion model based on CNN

Figure 6: Stages of image fusion model based on CNN (feature extraction, fusion, and reconstruction)

As illustrated in Fig. 6, the input images (Y1 and Y2) are forwarded to separate channels stage C1 and C2 where C1 and C2 are the feature extraction. The C1 contains C11 and C12 that contain 5 × 5 filters for feature extraction at low levels such as edges and corners. C11, C12, C21, and C22 participate in the same weights, which are considered pre-fusion channels. This architecture has a three-fold advantage: first, the same features for the input images are forced to learn the network. So, the output feature maps of C1 and C2 are similar in the type of features. Hence, the fusion layer is used to combine the respective feature maps in a simple manner. The resulting features are added with optimum performance rather than other gathering feature operators. In feature addition, the same types of features from the two images are fused together (see Fig. 7).

Figure 7: Visualization output of the learned filters with different numbers of iterations

• FSSIM Loss Function

This section proposes the process of computing loss without using a reference image. This process is carried out by FSSIM image quality measure [29]. Assume that {yk}={yk|k=1,2} denotes the set of image patches extracted at a location of the pixel p from the pair of the input images and yf denotes the extracted patch from the output of the CNN (fused image at same location p). The aim is a computation of a score to determine the performance of the fusion obtained by patches of the input yk and the fused image yf. The FSSIM function aims to measure the similarity between the input patches yk and fused image patches yf. There are three aspects of similarity: Contrast (c), Luminance (l), and Structure (s) are determined, and their product is used to calculate the overall index measured.

l(yk,yf)=2μykμyf+C1μyk2+μyf2+C1(5)

c(yk,yf)=2σykσyf+C2σyk2+σyf2+C2(6)

s(yk,yf)=σykyf+C3σykσyf+C3(7)

FSSIM(yk,yf)=[l(yk,yf)]α⋅[c(yk,yf)]β⋅[s(yk,yf)]γ(8)

where μG,μI,σG,σI, and σGI are the local means, standard deviations, and cross-covariance for the input image patch yk and the output image patch yf. C1, C2, and C3 are stabilization constants. If the parameters α = β = γ = 1 (the default for exponents), and C3 = C2/2 (default selection of C3), the simplified FSSIM index is obtained as follows:

FSSIM(yk,yf)=(2μykμyf+C1)(2σykyk+C2)(μ2yk+μ2yf+C1)(σ2yk+σ2yf+C2)(9)

The obtained score at a certain pixel p is:

score(p)=SSIM(yk,yf)(10)

Hence, the total loss is calculated as:

Loss=1N∑p∈Pscore(p)(11)

where N be the number of the total pixels in an image and P be the group of all pixels in the input image.

3.3 Image Enhancement

Image enhancement is a vital step for image preprocessing that it used to improve the image quality. The image enhancement techniques are implemented to optimize the illumination and enhance the features of the images. Different image enhancement techniques are applied in this paper to adjust the image quality and preserve the image details. The enhancement techniques utilized are HE, CLAHE, histogram matching, fuzzy enhancement, and fuzzy type Π. HE is applied to adjust the appearance of the image [30]. CLAHE is applied to medical images to increase the global contrast [31,32]. Histogram matching is used to improve the poor images that are corrected according to another image with good quality. Fuzzy technique and fuzzy type Type-II are applied to optimize the image features [33–38].

4  Fusion Quality Evaluation Metrics

The detailed information is evaluated by the average gradient, entropy, edge intensity, quality factor, standard deviation, local contrast, and PSNR. Visual inspection is considered one of the most significant tools used for evaluation. The proposed model evaluation is calculated by using various metrics.

5  Simulation Results and Comparative Study

The proposed model is evaluated by carrying out different simulation tests. The simulation experiments are performed by Python programming language, the Keras with Tensor Flow backend are involved in implementing the proposed CNN model, and the scikit-image library [27] for image processing issues is also utilized. This model is carried out on NVIDIA GTX 1050 GPU.

The proposed model has been applied to several image modalities [38], as illustrated in Fig. 8. The Gaussian filtering method based on key-points registering is performed for the medical image registration stage for all tested medical image datasets. Therefore, the resulting registered images introduce a high matching between areas in the input images. This generates more details contained in the fused image and increases the clarity of the image, as illustrated in Fig. 9.

Figure 8: The utilized medical datasets of several cases

Figure 9: The simulation subjective results of the proposed fusion approach in the case of without and with employing the registration process

Tab. 1 introduces the evaluation performance between the tested different medical image cases for the proposed model. To test the proposed medical image deep fusion approach, the effectiveness of the proposed approach is compared to the performance of the traditional PCA, DWT, Curvelet, NSCT, Fuzzy, SWT-based fusion techniques [20–25]. For example, the results are shown in Tab. 2 for simplicity of the tested medical images case 1.

To further evaluate the proposed medical image deep fusion approach on real medical image datasets, the proposed approach is evaluated by real data collected from Medical Imaging and Interventional Radiology department (MIIR) at Menoufia University, National Liver Centre in Egypt. This real data consists of medical images with different imaging (MR and CT) techniques, as illustrated in Fig. 10. Tab. 3 shows the evaluation metrics of the proposed medical image deep fusion approach for each case and the fused image of the tested real medical data presented in Fig. 10.

Figure 10: Real medical data with different modalities

The results reveal that the proposed model is efficient. Compared to other models, the proposed model achieves high performance. Also, the proposed model is recommended and efficient for real medical data.

Several enhancement techniques are applied to the resulting fused images to adjust the illumination of the medical images. The results show that the performance of the proposed model is enhanced. The performed enhancement techniques are HE, HM, CLAHE, fuzzy technique, and fuzzy type Π. The results clarify that the CLAHE and fuzzy technique are the best for medical image enhancement. But from the visual representation, the fuzzy technique is the best. Tabs. 4–15 show the evaluation metrics of the edge intensity, average gradient, and contrast of the enhancement techniques. Tabs. 16–20 show the evaluation metrics of the edge intensity, average gradient, and contrast of the enhancement techniques of the real cases.

After applying different enhancement techniques in the fused images, the results ensure that the fuzzy and CLAHE are the best of used enhancement techniques. This is because the visual representation of the images that result from the fuzzy technique is more obvious than the images that result from CLAHE. Also, after applying different enhancement techniques in the resulting fused images of the real cases, the results show that CLAHE and fuzzy are the best used enhancement techniques.

6  Conclusions

This paper presented an efficient medical image fusion model based on a deep CNN framework for different multi-modality medical images of standard and real medical data. The proposed model depends on extracting the different features from CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to get the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as HM, HE, fuzzy technique, fuzzy type Π, and CLAHE. Various metrics of the fusion quality measure the performance of the proposed fusion-based CNN model. So, the proposed medical image fusion approach has been evaluated by using several quality measures to demonstrate its validity and effectiveness compared to traditional fusion techniques. The proposed medical image deep fusion model has achieved high fusion performance. Several enhancement techniques are applied to the standard and real medical fused images to enhance the image features.

Acknowledgement: Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding Statement: Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

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