MRI Medical Images Research Articles

MRI Liver Image Assisted Diagnosis Based on Improved Faster R-CNN

In response to challenges in liver occupancy such a variety of types and manifestations and difficulties in differentiating benign and malignant ones, this paper takes liver images of enhanced MRI scan as the research object, targets on the detection and identification of liver occupancy lesion areas and determining if it is benign or malignant. Accordingly, the paper proposes an auxiliary diagnosis method for liver image combining deep learning and MRI medical imaging. The first step is to establish a reusable standard dataset for MRI liver occupancy detection by pre-processing, image denoising, lesion annotation and data augmentation. Then it improves the classical region-based convolutional neural network (R-CNN) algorithm Faster R-CNN by incorporating CondenseNet feature extraction network, custom-designed anchor size and transfer learning pre-training. This is to further improve the detection accuracy and benign and malignant classification performance of liver occupancy. Experiments show that the improved model algorithm can effectively identify and localise liver occupancies in MRI images, and achieves a mean average precision (mAP) of 0.848 and an Area Under the Curve (AUC) of 0.926 on the MRI standard dataset. This study has important research significance and application value for reducing manual misses and misdiagnosis and improving the early clinical diagnosis rate of liver cancer.

Self-supervised Anomaly Detection with Random-shape Pseudo-outliers.

Anomaly detection in a medical image is a challenging yet essential task. It relies on learning patterns/distributions from health data only, and no abnormal samples are available during training. This study proposes a novel self-supervised learning method to precisely detect and localize anomalies in MRI medical images. We synthesize abnormal images by overlaying random pseudo-outliers onto normal samples and propose a discriminative model for anomaly detection. Unlike prior arts that generate abnormalities with pre-determined regular geometric shapes, we introduce a new outlier synthesis strategy capable of generating random-shape anomalies. By learning the disentanglement of pseudo-outliers and normal regions in the synthesized images, our model can capture natural anomalies in images at both the pixel level and sample level. We present our empirical experimentation on two publicly accessible datasets and demonstrate the proposed method's superiority over SOTA solutions on MRIs.

Bioengineering in the Development of Artificial Hips, Knees, and other joints. Ultrasound, MRI, and other Medical Imaging Techniques

The bioengineering technique is developed as a method that is essential to combine different dead and inorganic materials with a living elements. Artificial organs are human-made organs that are helpful for providing a specific function for the patients to reach normal life soon. Improvement in healthcare can be possible to reach with properly aligned different artificial organs that are helping to overcome serious situations in life. In this research, the secondary qualitative method is used which is beneficial for completing the study in an effective way. Different previous published research in different journals, articles, and books are referred to for completing the research which is important for gaining proper growth for the research. Different aspects of bioengineering in the field of providing artificial body parts for humans are discussed in this research which is helping them to recover from any disease or impact of an accident. The overall impact on society and growth in the life developed due to artificial organs are also discussed in this research. Medical imaging techniques are helpful to create growth for research by providing the right track to growth. A proper understanding of diseases can be possible with the help of these technological advancements that are able to eliminate difficulties that are growing in human health. Keywords : artificial organs, Bioengineering, biomedical engineering development, MRI, prosthetics, replacing organs.

Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method.

With the continuous improvement of medical imaging equipment, CT, MRI and PET images can obtain accurate anatomical information of the same patient site. However, due to the fuzziness of medical image physiological evaluation and the unhealthy understanding of objects, the registration effect of many methods is not ideal. Therefore, based on the medical image registration model of Partial Volume (PV) image interpolation method and rigid medical image registration method, this paper established the non-rigid registration model of maximum mutual information Novel Partial Volume (NPV) image interpolation method. The proposed NPV interpolation method uses the Davidon-Fletcher-Powell algorithm (DFP) algorithm optimization method to solve the transformation parameter matrix and realize the accurate transformation of the floating image. In addition, the cubic B-spline is used as the kernel function to improve the image interpolation, which effectively improves the accuracy of the registration image. Finally, the proposed NPV method is compared with the PV interpolation method through the human brain CT-MRI-PET image to obtain a clear CT-MRI-PET image. The results show that the proposed NPV method has higher accuracy, better robustness, and easier realization. The model should also have guiding significance in face recognition and fingerprint recognition.

A fusion algorithm based on composite decomposition for PET and MRI medical images

Medical image fusion is of great significance in medical analysis and disease diagnosis. Until now, there are still limitations to the current fusion methods to our current knowledge, such as the restricted ability to extract detailed information and the luminance deterioration. In this paper, we propose a composite decomposition algorithm to overcome the limitations mentioned above, and which consists of the following steps. Firstly, an improved structure tensor (IST) is proposed to convert the MRI images to the smoothing layer, edges layer and corners layer, which ensures the resulting image has better brightness quality; Secondly, Non-Subsampled Shearlet Transform (NSST) is adopted to decompose the smoothing layer of MRI and the Y component of PET obtained from YUV transform into low- and high-frequency sub-bands images; The use of NSST can allow important detailed information to be preserved in the resulting image. Thirdly, the weighted averaged fusion rule guided by gradient and local energy (WAGLE) is introduced to fuse the low-frequency sub-bands, which can further improve the brightness of the fused images. The Whole Brain Atlas Database is used to evaluate the proposed method. The experimental results show that the algorithm proposed in this paper has significant improvement in the smoothness, color brightness, edge and local features of the images. Qualitative and quantitative analysis results show that the presented method has overcome the disadvantages of some current methods, which is superior to the other seven advanced fusion methods.

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network.

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat–top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for , CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters.

Controllable Medical Image Generation via GAN.

Medical image data is critically important for a range of disciplines, including medical image perception research, clinician training programs, and computer vision algorithms, among many other applications. Authentic medical image data, unfortunately, is relatively scarce for many of these uses. Because of this, researchers often collect their own data in nearby hospitals, which limits the generalizabilty of the data and findings. Moreover, even when larger datasets become available, they are of limited use because of the necessary data processing procedures such as de-identification, labeling, and categorizing, which requires significant time and effort. Thus, in some applications, including behavioral experiments on medical image perception, researchers have used naive artificial medical images (e.g., shapes or textures that are not realistic). These artificial medical images are easy to generate and manipulate, but the lack of authenticity inevitably raises questions about the applicability of the research to clinical practice. Recently, with the great progress in Generative Adversarial Networks (GAN), authentic images can be generated with high quality. In this paper, we propose to use GAN to generate authentic medical images for medical imaging studies. We also adopt a controllable method to manipulate the generated image attributes such that these images can satisfy any arbitrary experimenter goals, tasks, or stimulus settings. We have tested the proposed method on various medical image modalities, including mammogram, MRI, CT, and skin cancer images. The generated authentic medical images verify the success of the proposed method. The model and generated images could be employed in any medical image perception research.

Edge Detection Algorithm of MRI Medical Image Based on Artificial Neural Network

In order to be able to extract useful information from MRI medical images, we need to carry out effective image segmentation of MRI medical image and use appropriate operators to detect the edge of the image. Firstly, this paper adopts a research method based on the characteristics of horizontal, vertical and diagonal differences, and then carries out comparative analysis on different operators from different perspectives such as frequency domain and velocity domain. After analyzing the results, it is concluded that: Canny operator can detect complete, continuous and detailed edges at a faster speed. This paper uses edge detector based on Canny operator as the training output of artificial neural network. By training artificial neural network. The optimized parameters were obtained, and subjective analysis and computational analysis were performed on the quality of edge detection results of MRI medical images based on different methods. The results show that compared with the single Sobel, Canny and other traditional operators, the edge information of MRI medical image obtained by the neural network-based edge detection method is more complete and its processing time is nearly 3 times faster than the traditional edge detection method.

Medical images fusion using two-stage combined model DWT and DCT

The purpose of image enhancement is to improve the interpretation or perception of information in the image for viewers and the input of automated processing systems. Combining a multicenter image is a way of combining several images on a screen, focusing on different objects so that all objects appear in focus in the final image. Wavelet transform and cosine transform are used in many image processing applications, including image fusion. This paper's technique will combine DWT and DCT in two steps for medical MRI and PET images, eventually extracting the combined image. Input images are first divided into 8-pixel blocks in which DCT coefficients are extracted. After extracting the DCT coefficients, the first step of the combination takes place. The re-images will then be combined with the DWT conversion. According to the presented data, the proposed method achieved up to 5% better combination and, as a result, better image quality than the single-stage DWT and DCT methods.

Spine Medical Image Segmentation Based on Deep Learning.

The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image segmentation of 22 research objects. The application value of this algorithm in MRI image processing was comprehensively evaluated by accuracy (Acc), sensitivity (Sen), specificity (Spe), and area under curve (AUC). The results show that the image processing time of fully convolutional network (FCN) algorithm and U-Net algorithm is greater than 6 min, while the processing time of BN-U-Net algorithm is only 5–10 s, and the processing time is significantly shortened (P < 0.05). The Acc, Sen, and Spe results of BN-U-Net segmentation algorithm were 94.54 ± 3.56%, 88.76 ± 2.67%, and 86.27 ± 6.23%, respectively, which were significantly improved compared with FCN algorithm and U-Net algorithm (P < 0.05). In summary, the improved U-Net network algorithm used in this study significantly improves the quality of spinal MRI images by automatic segmentation of MRI images, which is worthy of further promotion in the field of spinal medical image segmentation.

Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images.

In this study, we introduced a preprocessing novel transformation approach for multifocus image fusion. In the multifocus image, fusion has generated a high informative image by merging two source images with different areas or objects in focus. Acutely the preprocessing means sharpening performed on the images before applying fusion techniques. In this paper, along with the novel concept, a new sharpening technique, Laplacian filter + discrete Fourier transform (LF + DFT), is also proposed. The LF is used to recognize the meaningful discontinuities in an image. DFT recognizes that the rapid change in the image is like sudden changes in the frequencies, low-frequency to high-frequency in the images. The aim of image sharpening is to highlight the key features, identifying the minor details, and sharpen the edges while the previous methods are not so effective. To validate the effectiveness the proposed method, the fusion is performed by a couple of advanced techniques such as stationary wavelet transform (SWT) and discrete wavelet transform (DWT) with both types of images like grayscale and color image. The experiments are performed on nonmedical and medical (breast medical CT and MRI images) datasets. The experimental results demonstrate that the proposed method outperforms all evaluated qualitative and quantitative metrics. Quantitative assessment is performed by eight well-known metrics, and every metric described its own feature by which it is easily assumed that the proposed method is superior. The experimental results of the proposed technique SWT (LF + DFT) are summarized for evaluation matrices such as RMSE (5.6761), PFE (3.4378), MAE (0.4010), entropy (9.0121), SNR (26.8609), PSNR (40.1349), CC (0.9978), and ERGAS (2.2589) using clock dataset.

Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods

Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.

Assessment of 3D Lumbosacral Vascular Anatomy for OLIF51 by Non-Enhanced MRI and CT Medical Image Fusion Technique.

Study design: Prospective study. Objective: Medical image fusion can provide information from multiple modalities in a single image. The present study aimed to determine whether three-dimensional (3D) lumbosacral vascular anatomy could be adequately portrayed using a non-enhanced CT–MRI medical image fusion technique. Summary of Background Data: Lateral lumbar interbody fusion has gained popularity for the surgical treatment of adult spinal deformity (ASD). Oblique lumbar interbody fusion at L5–S1 (OLIF51) is receiving considerable attention as a method of creating good L5–S1 lordosis. Access in OLIF51 requires evaluation of the vascular anatomy in the lumbosacral region. Conventional imaging modalities need a contrast medium to describe the vascular anatomy. Methods: Participants comprised 15 patients with ASD or degenerative lumbar disease who underwent corrective surgery at our hospital between January 2020 and June 2021. A 3D vascular image with bony structures was obtained by fusing results from MRI and CT. We processed the merged image and measured the distance between left and right common iliac arteries and veins at two levels: the lower end of the L5 vertebral body (Window A) and the upper end of the S1 vertebral body (Window B). Results: The mean sizes of Window A and Window B were 29.7 ± 10.7 mm and 36.9 ± 10.3 mm, respectively. The mean distance from the bifurcation to the lower end of the L5 vertebra was 23.7 ± 10.9 mm. Coronal deviation of the bifurcation was, from center to left, 12.6 ± 12.3 mm, and the distance from the center of the L5 vertebral body to the bifurcation was 0.79 ± 7.3 mm. Only one case showed a median sacral vein (6.7%). Clinically, we performed OLIF51 in 12 of the 15 cases (80%). Conclusion: Evaluating 3D lumbosacral vascular anatomy using a non-enhanced MRI and CT medical image fusion technique is very useful for OLIF51, particularly for patients in whom the use of contrast medium is contraindicated.

PCA based SVD fusion for MRI and CT medical images

This paper presents a new approach to the multi-modal medical image fusion based on Principal Component Analysis (PCA) and Singular value decomposition (SVD).The main objective of the proposed approach is to facilitate its implementation on a hardware unit, so it works effectively at run time. To evaluate the presented approach, it was tested in fusing four different cases of a registered CT and MRI images. Eleven quality metrics (including Mutual Information and Universal Image Quality Index) were used in evaluating the fused image obtained by the proposed approach, and compare it with the images obtained by the other fusion approaches. In experiments, the quality metrics shows that the fused image obtained by the presented approach has better quality result and it proved effective in medical image fusion especially in MRI and CT images. It also indicates that the paper approach had reduced the processing time and the memory required during the fusion process, and leads to very cheap and fast hardware implementation of the presented approach.

Medical Image Segmentation Algorithm Based on Multi-scale Color Wavelet Texture

In order to improve the segmentation accuracy of brain tumor magnetic resonance medical image, a segmentation method of brain tumor magnetic resonance medical image based on multi-scale color wavelet texture features is proposed. The segmentation model of brain tumor magnetic resonance medical image is established, and the motion damage information of brain tumor magnetic resonance medical image is adaptively fused in the ultrasound imaging environment. The medical image information is enhanced by using the motion skeletal muscle block matching technology. According to the suspicious point feature matching method of brain tumor, the fusion detection and processing of brain tumor magnetic resonance medical image are carried out. The multi-scale color wavelet texture feature detection method is used to extract the image features of brain tumor MRI points, and the CT bright spot features are used to analyze the features of brain tumor MRI medical images. Combined with the adaptive neural network training method, the automatic detection of brain tumor magnetic resonance medical image is completed, and the suspected brain tumor points are extracted, so as to realize the segmentation of brain tumor magnetic resonance medical image. Simulation results show that the proposed method can effectively improve the segmentation accuracy of brain tumor MRI medical image, and has high resolution and accuracy for suspicious brain tumor detection.

Novel high level retrieval system based on mathematic algorithm & technique for MRI medical imaging and classification

A novel Brain MRI Medical imaging method for the retrieval and classification of MRI Medical database is presented in this article. For each MRI medical image, the multi-resolution modified spectral correlation function transform is computed and the average and standard deviation are used as the textural properties. The edge detectors and modified bit-based histogram defined the high level properties. A feedback-based dynamic weighting of shape and textural features' composition generate a resistant feature vector for classification of MRI medical image. For MRI medical image similarity and classification procedure, a unified and comprehensive matching scheme on the basis of matrix error rate method was performed. The feature vectors size in the presented algorithm is the least one assessed to different methods. Also, the previous published techniques calculation time is more compared to the proposed algorithm which is an advantage over the presented Novel technique. The findings show that new algorithm enjoys more precious in classification and retrieval and efficiency in evaluation with other algorithms and methods at the MRI Medical image database.

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

A brain tumor is a problem that threatens life and impedes the normal working of the human body. The brain tumor needs to be identified early for the proper diagnosis and effective treatment planning. Tumor segmentation from an MRI brain image is one of the most focused areas of the medical community, provided that MRI is non-invasive imaging. Brain tumor segmentation involves distinguishing abnormal brain tissue from normal brain tissue. This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques, interbreeding. It requires presentation and quantitative analysis, from standard segmentation and classification methods to the best class strategies.

A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation

The automatic segmentation of brain tumour from MRI medical images is mainly covered in this review. Recently, state-of-the-art performance is provided by deep learning-based approaches in the field of image classification, segmentation, object detection, and tracking tasks. The core feature deep learning approach is the hierarchical representation of features from images and thus avoiding domain-specific handcrafted features. In this review paper, we have dealt with a Review of Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation. First, we have discussed basic architecture and approaches for deep learning methods. Secondly, we have discussed the literature survey of MRI brain tumour segmentation using deep learning methods and its multimodality fusion. Then, the advantages and disadvantages of each method analyzed and finally concluded the discussion with the merits and challenges of deep learning techniques. The review of brain tumour identification using deep learning Techniques may help the research to have a better focus on it.

De-noising and enhancement of MRI medical images using Gaussian filter and histogram equalization

Noise in Medical Images reduces the image contrast, resolution, and is observed as an obstacle in image understanding. MRI is used for the treatment of brain, ankle, foot, and prostate cancers. Normally, MR Images consist of Gaussian Noise, Poisson Noise, Speckle Noise, Salt and Pepper Noise, etc. In Medical Image Processing, removing noise and obtaining information from MR Images is a crucial and challenging task. An accurate image is very important for further diagnosis or post-processing tasks such as visualization, segmentation, etc. For removing different types of noise from MR images, different noise filtering algorithms are used. Selecting a particular filtering method is application dependent. Gaussian Filter is the best technique for de-noising of medical images efficiently. To reveal the detailed information hidden in a very small range of grey level or color level from a low contrast image, a powerful tool such as a contrast enhancement algorithm is used for image enhancement to increase the visual appearance or quality of the image. Before Medical Image Analysis, improving the contrast of Medical Images is necessary using contrast enhancement methods. Histogram Equalization (HE) is an important and widely used technique for enhancing the contrast of an image among the different contrast enhancement methods. This paper explains the method of de-noising and image contrast enhancement using Gaussian filter and HE and performed simulations using different databases such as IRMA, KAGGLE, and WANG, etc. and observed that Gaussian filter is used to reduce Gaussian noise efficiently for removal of Gaussian noise by smoothing inner distinct image region and also in Histogram Equalization, the histogram of the resultant image is flat.

Fast and denoise feature extraction based ADMF–CNN with GBML framework for MRI brain image

The filtration through machine learning approaches can solves many problems in diagnosis of MRI brain. Various medical image applications are available in usage but they are offering limited solutions at diagnosis process. Therefore disease diagnosis performance is improved by proposed CNN (convolution neural networks)–GB (Gradient Boosting) and adaptive median filter (AMF) mechanism. In this work, brain-related disorders have been identified by using deep learning and machine learning technique. So brain-correlated information purpose real-time MRI medical images and “FIGSHARE”, “OASIS” datasets are collected. These are having more information of disease identification and classification. Various filters like Gaussian filter, median filters unable to remove the noise in medical images, when density of noise is more than 78% so that an advanced filters design is necessary for a fast and accurate brain disease diagnosis. In this research CNN–ML based adaptive median filter is designed for noise exclusion and effective diseases identification. PSNR, MSE, True positive rate and accuracy parameters can describe the application efficiency. MRI brain diseases investigation with CNN–AMF and GBML model has achieved 0.9863-accuracy and 0.9832-True Positive Rate, which is a better improvement. This implemented design and improved results are compete with current medical research applications, also useful for doctors, researchers and Diagnosis centers.