Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image. The proposed techniques consider the values of Peak Signal to Noise Ratio (PSNR) and the level of noise as inputs to the attention-U-Net model for segmentation of the tumor. The framework has been divided into three stages: removing speckle and Rician noise, the segmentation stage, and the feature extraction stage. The framework presents solutions for each problem at a different stage of the segmentation. In the first stage, the framework uses Vibrational Mode Decomposition (VMD) along with Block-matching and 3D filtering (Bm3D) algorithms to remove the Rician. Afterwards, the most significant Rician noise-free images are passed to the three different methods: Deep Residual Network (DeRNet), Dilated Convolution Auto-encoder Denoising Network (Di-Conv-AE-Net), and Denoising Generative Adversarial Network (DGAN-Net) for removing the speckle noise. VMD and Bm3D have achieved PSNR values for levels of noise (0, 0.25, 0.5, 0.75) for reducing the Rician noise by (35.243, 32.135, 28.214, 24.124) and (36.11, 31.212, 26.215, 24.123) respectively. The framework also achieved PSNR values for removing the speckle noise process for each level as follows: (34.146, 30.313, 28.125, 24.001), (33.112, 29.103, 27.110, 24.194), and (32.113, 28.017, 26.193, 23.121) for DeRNet, Di-Conv-AE-Net, and DGAN-Net, respectively. The experiments that have been conducted have proved the efficiency of the proposed framework against classical filters such as Bilateral, Frost, Kuan, and Lee according to different levels of noise. The attention gate U-Net achieved 94.66 and 95.03 in the segmentation of free noise images in dice and accuracy, respectively.
Image segmentation is a crucial challenge in computer vision and image processing alike. For images, segmentation generally means dividing them into mutually exclusive areas to make them easy for analysis and examination [
The brain is one of the vital organs in the human body, so brain diseases need quick examination and diagnosis. The early detection of brain tumors participates in the process of diagnosis. The main reason for this type of segmentation is to generate an accurate delineation of brain tumor areas with correctly located masks [
The main objective of this paper is to introduce a novel framework for brain tumor segmentation from MRI images, as well as to reduce the speckle and Rician noise. The novel’s framework consists of two principal stages. The first is image enhancement, which is used to reduce the speckle and Rician noise, and the second stage is MRI image segmentation using attention U-Net. The framework starts by reducing the Rician and speckle noise from MRI to remove the unnecessary signal from MRI images. Reducing noise stages helps to reduce the loss of vital information in MRI, especially in small images [
The second stage in the proposed framework is called the segmentation stage for the denoised MRI using the attention U-Net model. The recent research in applying learning approaches such as machine learning (ML) and deep learning (DL) to the segmentation process has become a significant direction in offering quick medical treatment to patients [
The contributions of this paper can be summarized as follows:
Demonstrating a novel framework for enhancing the quality of MRI images by reducing noises that are called speckle and Rician noise. Introducing a hyper model of VMD-BM3D for Rician noise and (DeRNet, Di-Conv-AE-Net, DGAN-Net) methods for diminishing noise and increasing accuracy. Minimizing noise using VMD and different modified methods has cooperated in the process of increasing the accuracy of attention U-Net compared to other available models. Achieving better accuracy than other compared models such as U-Net and residual U-Net.
The rest of this paper is organized as follows: Section 2 introduces the methodology and describes the main stages in the proposed framework, which are: importing MRI images as a dataset, reducing both Rician and speckle noise; and the segmentation process using the U-Net model. Section 3 demonstrates the discussion and experimental results. Section 4 is the paper’s conclusion.
Steps of methodology:
First step: image acquisition: the novel framework used an MRI dataset from Kaggle.
Second step: image enhancement: this step contains two different stages.
Stage A: reducing Rician noise using VMD or BM3D. Stage B: reducing speckle noise using different methods (Conv-AE-Net, D-U-Net, Br-U-Net, DGan-Net, and DeRNet)
Step3rd: segmentation: Using attention U-Net architecture as illustrated in
The pseudocode of the segmentation process:
The novel methodology used a dataset from Kaggle called the Low Grad Glioma (LGG) Segmentation Dataset, which contains brain MRI images together with manual fluid-attenuated inversion recovery (FLAIR) abnormality masks. The medical images were collected from The Cancer Imaging Archive for a greater number of cancer cases contained in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with at least FLAIR sequence and genomic cluster data available.
Reducing noise plays a vital role in the framework. This step is divided into two stages. The first stage is removing the Rician noise using VMD, and the second stage is removing the speckle noise using different modified models like BM3D, Di-Conv-AE-Net, D-U-Net, Br-U-Net, DGan-Net, and DeRNet.
In this subsection, we added speckle noise and Rician noise for MRI for testing and training. And the Rician noise is added using
For adding the speckle noise, the original image expressed by G(x, y), f (x, y) is used to express noisy image and the multiplicative and additive noises are
Classification of MRI denoising methods 3-dimensional discrete wavelet transformation and wavelet shrinkage. The MRI denoising method is divided into two stages. The first stage is the filtering, and the second is called the Transform Domain. The filtering domain is also linear and nonlinear, the linear is for the spatial and transform domain, and the nonlinear is for the combination of domain and range filters, Anisotropic Diffusion Filtering (ADF), and Non-Local Means (NLM). The second part is called the transform domain, which is divided into wavelet, courvalet, and contourlet [
This method has been used in the novel methodology to reduce the Rician noise. The method divides the MRI images into different frequency components, such as low frequency and high frequency. The mechanism of the algorithm starts by removing the higher frequency and using the lower frequency to retrieve the image. The last step in the algorithm is to use a total variation (tv)-regularization to remove remnant noise details from the first stage [
BM3D is a modified method for removing the Rician noise. It’s used to reduce the Rician from the MRI 3D images. The mechanism of BM3D is divided into two stages. The first stage is used to group wavelet transforms, noise invalidation denoising, and produce the inverse for the 3D wavelet transform. Stage 2 uses the signal and inverse signal with a wiener filter and winner coefficient to produce an inverse 3D wavelet. In the final step, the mechanism uses inverse variance stabilisation transform (VST) and clahe to produce the denoised image as shown in
The DeRNet model has been proposed to handle the vanishing gradient issue. DeRNet minimizes gradients by creating shortcut connections between layers to resume the process of learning and training. This model uses 25 layers, and the overall block contains two stages: the res-block and the overall block [
The DGAN-Net model is considered a modified form of the traditional Generative Adversarial Network (GAN) model. DGAN-Net consists of two layers, which are the discriminator and the generator. The generator layer is used for generating fake MRI images to be used in the process of training, while the other part is used for differentiating between the fake and real MRI images. The Discriminator Deep Network uses zero padding, batch normalization, and rectifier (Relu) to differentiate between fake and real images [
This modified version of the autoencoder model is used to deal with the small image to maximize the image to avoid losing details of the MRI images. In the CNN model, operations are done in two stages; the first stage is called subsampling, and the second stage is called down-sampling. The subsampling layers represent the content of the image, while the down-sampling layers allow the receptive field to expand. This causes a big problem because the resolution of the MRI image is very important in removing the speckle noise process. The modified version of the auto-encoder avoids losing any details because it keeps the resolution of the MRI image by maximizing the number of parameters of the filter and the number of operations of location, which is still the same [
In this step, the proposed framework uses the modified version from U-Net for segmenting the images after removing speckle and Rician noise in the previous steps. The modified version of U-Net, which is called the attention gate U-Net, can deal with different structures of medical images that may vary in size and shapes. Attention U-Net enables us to get over the need to use explicit external organ localization [
The results and discussion section are divided into three sections: the first stage shows the results of reducing Rician noise; the second section shows the results of reducing the speckle noise, and the last section shows the results of segmentation of MRI images using the attention U-Net model.
In this part of the paper, we show the results of removing the Rician noise using VMD and BM3D as mentioned in
Method name | PSNR (dB) | SSIM | Epoch time | ||||||
---|---|---|---|---|---|---|---|---|---|
0.1 | 0.25 | 0.5 | 0.75 | 0.1 | 0.25 | 0.5 | 0.75 | ||
VMD | 35.243 | 32.135 | 28.214 | 24.124 | 93.21% | 90.81% | 88.24% | 80.24% | 13 |
Bm3D | 36.11 | 31.212 | 26.215 | 24.123 | 93.67% | 90.21% | 88.11% | 80.24% | 31 |
Method name | PSNR (dB) | SSIM | Epoch time | ||||||
---|---|---|---|---|---|---|---|---|---|
0.1 | 0.25 | 0.5 | 0.75 | 0.1 | 0.25 | 0.5 | 0.75 | ||
Original image (Noisy) | 15.467 | 13.158 | 12.135 | 11.235 | 83.35% | 62.35% | 58.14% | 50.14% | |
Bilateral method | 18.241 | 15.243 | 13.467 | 11.555 | 82.68% | 67.11% | 59.11% | 52.11% | 3.9 |
Frost | 18.124 | 15.421 | 13.101 | 11.961 | 81.12% | 65.11% | 58.25% | 51.16% | 1.99 |
Kuan | 17.111 | 14.101 | 13.811 | 12.781 | 81.14% | 66.16% | 61.254% | 53.11% | 2.45 |
Lee | 18.246 | 17.211 | 14.124 | 13.742 | 83.22% | 79.13% | 73.241% | 64.121% | 3.77 |
Auto encoder | 25.233 | 22.151 | 19.149 | 18.118 | 86.11% | 84.13% | 82.55% | 73.11% | 11 |
Method name | PSNR SSIM | |
---|---|---|
DeRNet + BM3D | 31.251 | 93.46% |
30.331 | 92.73% | |
30.222 | 92.12% |
Method name | PSNR SSIM | |
---|---|---|
DeRNet + VMD | 29.421 | 91.25% |
28.123 | 90.45% | |
27.431 | 90.32% |
In this subsection, the novel framework shows the final results after using the attention-U-Net deep learning model for segmentation of the MRI images after reducing the speckle and Rician noise in the previous stages. The attention U-Net with the previous method of reducing the noise has achieved an accuracy of nearly 94.66, which is better than the accuracy of U-Net and the accuracy without the previous model for removing the noise.
Segmentation of brain tumors is one of the most significant tasks for brain clinicians. Segmentation helps clinicians detect tumors in their early stages. The process of segmentation of brain tumors is divided into a set of stages such as image gathering, image enhancement, image segmentation, and feature extraction. To achieve final accuracy in the segmentation of MRI images, the methodology must take into consideration all details in each step because each stage’s accuracy can change the final result of segmentation. The process of MRI image enhancement and reducing all types of noise, such as speckle noise and Rician, plays a principal role in the accuracy of final segmentation. The novel framework has used more than one algorithm for reducing noise for each type of noise, such as VMD and BM3D for reducing Rician noise and more than one modified algorithm for reducing speckle noise. The framework chooses the best-qualified image in each stage. Finally, the framework used the attention U-Net model for segmenting the images from previous stages.