Adaptive Medical Image Deep Color Perception Algorithm

Image registration is a vital problem in medical imaging. It has many potential applications in clinical diagnosis (e.g. diagnosis of cardiac, retinal, pelvic, renal, abdomen, liver and tissue disorders). In this paper a new technique has been proposed based on feature based image registration using SURF (Speeded up robust features) and Hessian matrix feature extraction techniques for detecting, extracting and matching corresponding points in the reference (fixed) and source image (deformed). The source image is aligned using SURF and Hessian feature extraction techniques to align with the reference image. The proposed technique is based on multimodality of images, feature based classification and non-rigid images as the images taken are from different sources like MRI, CT scan and X-ray. The proposed technique uses feature-based methods to find correspondence between image features such as points, lines and contours. The non-rigid images use fixed (source) image and also deformable (target) image of the subject and aligns target image according to source/reference image.

A non-rigid medical image registration method based on improved linear elastic model

Magnetic resonance imaging plays a crucial role in clinical diagnosis due to its ability to provide information on soft tissue structure. At present, multi-contrast super-resolution (SR) methods for magnetic resonance (MR) images have been widely studied and have achieved good results. However, most studies overlook the impact of modal differences between reference and low-resolution (LR) image features on feature reconstruction, which may result in inaccurate reconstruction of detail features due to improper alignment of structural features. To address this issue, we propose a high-frequency feature transfer network for MR image SR task (HFTN-SR), which consists of two feature extraction branches and one high-frequency feature transfer branch. Considering the modal differences between the target and reference images, a high-low frequency decomposition method is used to decompose the reference image into high-frequency and low-frequency components, where the low-frequency components are used in the subsequent network to match LR target image features. A feature extraction block (FEB) is constructed to extract and integrate high-frequency and low-frequency features of the reference image, as well as features of the LR target image. In response to the modal differences between two image features, a feature transfer block (FTB) is designed to establish the correlation between the low-frequency features of the reference image and the target image features, and use the correlation matrix to transfer the high-frequency features of the reference image to the target image features. To further reduce the loss of shallow features caused by the increase in network depth, a standardized combined residual feature module (SCRFM) is constructed to supplement the shallow features of the target image into the final reconstructed features. Experiments on the public dataset FastMRI and the self-built dataset AXA show that the performance of HFTN-SR is superior to some state-of-the-art (SOTA) methods. Notably, HFTN-SR achieves the highest PSNR and SSIM scores across all tested datasets, with significant improvements in visual quality and detail reconstruction.

For patients with color vision defects, owing to the destruction of cone cells and loss of function, some color information is lost, hence changing the originally transmitted information by the image. The purpose of the traditional method of correction is to help patients distinguish between colors, but it does not take into account the problem of the saliency of the image. In this paper, we propose a saliency consistency-based image re-colorization for color blindness. We use image retrieval methods to select a large number of images and use co-saliency methods to detect salient areas of standard color images and color-blind simulated images. According to the detection results, an image with the same detection result was selected as the reference image. We grayscale the significantly changed image, recolor the grayscale image using the reference image, and the color scheme of the recolored image is similar to the reference image. The color matching scheme of the reference image makes the significance of the image basically unchanged in standard vision and color vision defects, thereby making the color blindness patient's perception of the image close to standard vision. We invite green blind patients to evaluate CVD simulation images with different recoloring methods subjectively. In addition, we use different evaluation criteria to evaluate the experimental results objectively. In the subjective evaluation and objective evaluation, the method proposed in this paper has achieved good results, which validates the effectiveness of our method.

BI&T Tell me about the Medical Imaging and Technology Alliance. David Fisher The Medical Imaging & Technology Alliance (MITA), a division of the National Electrical Manufacturers Association (NEMA), is the leading organization and collective voice of medical imaging equipment manufacturers, innovators, and product developers. It represents companies whose sales comprise more than 90 percent of the global market for medical imaging technology. The goals of MITA are to: • Increase the awareness and understanding of the value of medical imaging • Achieve efficient and reasonable regulation of medical imaging technologies • Interact with appropriate government agencies on reimbursement and technology assessment policies • Expand the global acceptance of the digital communications standard (DICOM) that allows digital imaging technologies to interact seamlessly • Improve regulatory harmonization of the global market for medical imaging products • Develop and represent industry positions in technical, trade and other issues • Provide market data unique to this industry

The benefits of medical imaging are enormous. Medical images provide considerable amounts of anatomical information and this facilitates medical practitioners in performing effective disease diagnosis and deciding upon the best course of medical treatment. A transition from traditional monochromatic medical images like CT scans, X-Rays, or MRI images to a colored 3D representation of the anatomical structure could further aid medical professionals in extracting valuable medical information. The proposed framework in our research starts with performing color transfer by finding deep semantic correspondence between two medical images: a colored reference image, and a monochromatic CT scan or an MRI image. We extend this idea of reference based colorization technique to perform colored volume rendering from a stack of grayscale medical images. Furthermore, we also propose to use an effective reference image recommendation system to aid for selection of good reference images. With our approach, we successfully perform colored medical volume visualization and essentially eliminate the painstaking process of user interaction with a transfer function to obtain color parameters for volume rendering.

Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data. However, networks trained on synthetic data often fail to generalize to real data. Cinematic rendering simulates the propagation and interaction of light passing through tissue models reconstructed from CT data, enabling the generation of photorealistic images. In this paper, we present one of the first applications of cinematic rendering in deep learning, in which we propose to fine-tune synthetic data-driven networks using cinematically rendered CT data for the task of monocular depth estimation in endoscopy. Our experiments demonstrate that: (a) convolutional neural networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model. Our empirical evaluation demonstrates that networks fine-tuned with cinematically rendered data predict depth with 56.87% less error for rendered endoscopy images and 27.49% less error for real porcine colon endoscopy images.

Fourier phase retrieval problem is equivalent to the recovery of a two-dimensional image from its autocorrelation measurements. This problem is generally nonlinear and nonconvex. Good initialization and prior information about the support or sparsity of the target image are often critical for a robust recovery. In this paper, we show that the presence of a known reference image can help us solve the nonlinear phase retrieval problem as a sequence of small linear inverse problems. Instead of recovering the entire image at once, our sequential method recovers a small number of rows or columns by solving a linear deconvolution problem at every step. Existing methods for the reference-based (holographic) phase retrieval either assume that the reference and target images are sufficiently separated so that the recovery problem is linear or recover the image via nonlinear optimization. In contrast, our proposed method does not require the separation condition. We performed an extensive set of simulations to demonstrate that our proposed method can successfully recover images from autocorrelation data under different settings of reference placement and noise.

Telemedicine is a technology-based substitute for conventional health care facilities. It symbolizes the practice of medication via indirect means. The foremost requirement for such remote healthcare practices is the communication of highly sensitive data over insecure networks, which demands very high protection of different types of health-related information such as Electronic Patient Record (EPR) and Related Medical images. Consequently, there is a stern need to develop algorithms to secure all confidential information for overall well being. This paper is an effort to propose one multilayer protection mechanism, which not only secures the patient’s transcript but also protects associated medical Images. EPR is embedded in the original medical image resulting in a watermarked medical image, which is further hidden in the reference image. But these two relevant records are given multiple strata of security before concealing and exposed to the insecure channel. Compression and Quantum Encryption of EPR are done, which is then embed into LWT transformed Medical Image, followed by scrambling and compression of all the planes of a watermarked medical image before embedding in different bands of LWT (Lifting Wavelet Transform), transformed reference image. This stride of hiding image is mandatory because original and watermarked medical images are very similar, thus prone to attacks. In order to avoid this, an image steganography technique is used to change the watermarked medical image’s visual structure, which is also referred to as visually meaningful encryption, as popular encryption algorithms give noise-like textures that can attract attackers. For authentication on the accession of records, biometric-based detection along with hash algorithm is also incorporated. Overall results (implemented using MATLAB) show that the proposed mechanism provides a highly secure and authentic medical healthcare system.

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

We present a novel approach to change detection between two brain MRI scans (reference and target.) The proposed method uses a single modality to find subtle changes; and does not require prior knowledge (learning) of the type of changes to be sought. The method is based on the computation of a local kernel from the reference image, which measures the likeness of a pixel to its surroundings. This kernel is then used as a feature and compared against analogous features from the target image. This comparison is made using cosine similarity. The overall algorithm yields a scalar dissimilarity map (DM), indicating the local statistical likelihood of dissimilarity between the reference and target images. DM values exceeding a threshold then identify meaningful and relevant changes. The proposed method is robust to various challenging conditions including unequal signal strength.

Image fusion is the process of gathering all the important information of multiple images into a single fused image. It is used in many fields like medical, military, remote sensing etc... Image fusion in a medical field is extensively used by physicians for analysis and treatment, as the fused image contains complementary features present in different medical images obtained from imaging devices. The wave atom transform based medical image fusion is proposed as the potential capabilities of wave atoms have been exploded in many applications like image denoising, fingerprint identification, compression etc. Medical image techniques became important tool for medical diagnosis and treatment. Medical images of different modalities such as MRI, CT, PET, SPECT provide information in a limited domain. In medical resources, MRI and CT images contain structural information whereas PET and SPECT images contain functional Information. Thus, single modality does not provide complete and accurate information, both structural as well as functional information is required. So far, many medical image fusion methods have been developed. The easiest way to perform image fusion is to perform weighted average of input images pixel by pixel. But this method produces side effects such as artifacts & contrast reduction. The proposed method is experimented on various medical images and compared with recent state of fusion methods. Results prove that images obtained from proposed method have better clarity and enhanced information and are practically more helpful for better treatment and quick analysis.

This research aims to evaluate the performance of neural networks in enhancing medical images and to explore the possibility of applying them in improving the quality of medical images for tumors, thereby enhancing diagnostic accuracy and treatment effectiveness. The study utilized a medical image of the lumbar spine MRI containing a tumor, to which Gaussian noise was added at levels of (0.01, 0.05, 0.10, 0.20). Noise removal techniques were applied using three linear filters: Mean Filter, Median Filter, and Gaussian Filter, in addition to the pre-trained deep neural network (DnCNN) for noise removal and image quality enhancement. To achieve these objectives, the deep learning tool library in MATLAB was used. Two evaluation methods were employed: the first was the visual inspection of the enhanced images, and the second included image quality improvement metrics such as PSNR, SSIM, and MSE. The results showed that the neural network (DnCNN) was able to improve the quality of medical images better than the other mentioned methods, achieving values of (66.7711, 0.6886, 0.0013) for (PSNR, SSIM, MSE) respectively at the low noise level of 0.01. It was observed that all the methods used faced challenges at higher noise levels. Thus, the results indicate that deep neural networks are effective in improving medical images and can be applied to enhance images related to spinal tumors, contributing to increased diagnostic accuracy and faster tumor detection, thereby improving healthcare. Keywords: Deep Neural Networks, Medical Images, Improving, DnCNN, Denoised تطبيقات الشبكات العصبية العميقة في تحسين الصور الطبية للأورام الشوكية الملخص يهدف هذا البحث إلى تقييم أداء الشبكات العصبية في تحسين الصور الطبية، واستكشاف إمكانية تطبيقها في مجال تحسين جودة الصور الطبية للأورام، مما يعزز دقة التشخيص وفاعلية العلاج، استخدمت الدراسة صورة طبية للعمود الفقري من النوع MRI ) ) في المنطقة القطنية تحتوي على ورم، حيث تم إضافة ضوضاء جاوس إلى الصورة بنسب ( 0.01، 0.05، 0.10، 0.20) تم استخدام تقنيات إزالة الضوضاء بواسطة ثلاثة فلاتر خطية، وهي Mean Filter وMedian Filter و Gaussian Filter ، بالإضافة إلى الشبكة العصبية العميقة ( DnCNN ) المدربة مسبقًا لإزالة الضوضاء وتحسين جودة الصور. لتحقيق هذه الأهداف، تم استخدام مكتبة أدوات التعلم العميق في MATLAB. كما تم استخدام طريقتين للتقييم: الأولى هي المعاينة البصرية للصور الناتجة عن التحسين، والثانية تشمل مقاييس تحسين جودة الصور مثل PSNR و SSIM و MSE . أظهرت النتائج أن الشبكة العصبية (DnCNN) قادرة على تحسين جودة الصور الطبية بشكل افضل من الطرق المذكورة الاخرى حيت حققت (66.7711 ، 0.6886 ، 0.0013 ) لكلا من (PSNR ،SSIM ، MSE ) على التوالي و عند نسبة ضوضاء المنخفظة 0.01)) . ولوحظ أن جميع الطرق المستخدمة تواجه تحديات عند مستويات الضوضاء المرتفعة. وبذلك تشير النتائج إلى أن الشبكات العصبية العميقة فعالة في تحسين الصور الطبية، وقابلة للتطبيق في تحسين الصور الخاصة بأورام العمود الفقري، مما يسهم في زيادة دقة التشخيص وسرعة اكتشاف الأورام، وبالتالي تحسين الرعاية الصحية. الكلمات المفتاحية : الشبكات العصبية العميقة ، الصور الطبية ، تحسين، DnCNN، ازالة الضوضاء .

This paper aims to develop a computer aided diagnosis (CAD) based on a two-step methodology to register and analyze pairs of temporal mammograms. The concept of "medical file", including all the previous medical information on a patient, enables joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The developed registration method aims to superimpose at best the different anatomical structures of the breast. The registration is designed in order to get rid of deformation undergone by the acquisition process while preserving those due to breast changes indicative of malignancy. In order to reach this goal, a referent image is computed from control points based on anatomical features that are extracted automatically. Then the second image of the couple is realigned on the referent image, using a coarse-to-fine approach according to expert knowledge that allows both rigid and non-rigid transforms. The joint analysis detects the evolution between two images representing the same scene. In order to achieve this, it is important to know the registration error limits in order to adapt the observation scale. The approach used in this paper is based on an image sparse representation. Decomposed in regular patterns, the images are analyzed under a new angle. The evolution detection problem has many practical applications, especially in medical images. The CAD is evaluated using recall and precision of differences in mammograms.

Registration of medical imaging is essential for aligning in time and space different modalities and hence consolidating their strengths for enhanced diagnosis and for the effective planning of treatment or therapeutic interventions. The primary objective of this study is to develop an integrated registration method that is effective for registering both brain and whole-body images. We seek in the proposed method to combine in one setting the excellent registration results that FMRIB Software Library (FSL) produces with brain images and the excellent results of Statistical Parametric Mapping (SPM) when registering whole-body images. To assess attainment of these objectives, the following registration tasks were performed: (1) FDG_CT with FLT_CT images, (2) pre-operation MRI with intra-operation CT images, (3) brain only MRI with corresponding PET images, and (4) MRI T1 with T2, T1 with FLAIR, and T1 with GE images. Then, the results of the proposed method will be compared to those obtained using existing state-of-the-art registration methods such as SPM and FSL. Initially, three slices were chosen from the reference image, and the normalized mutual information (NMI) was calculated between each of them for every slice in the moving image. The three pairs with the highest NMI values were chosen. The wavelet decomposition method is applied to minimize the computational requirements. An initial search applying a genetic algorithm is conducted on the three pairs to obtain three sets of registration parameters. The Powell method is applied to reference and moving images to validate the three sets of registration parameters. A linear interpolation method is then used to obtain the registration parameters for all remaining slices. Finally, the aligned registered image with the reference image were displayed to show the different performances of the 3 methods, namely the proposed method, SPM and FSL by gauging the average NMI values obtained in the registration results. Visual observations are also provided in support of these NMI values. For comparative purposes, tests using different multi-modal imaging platforms are performed.