Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images

To maximize the accuracy of classification for medical images especially in chest- X ray, we need to improve quality of CXR images or high resolute images will be needed. Pneumonia is a lung infection caused by organism like bacteria or virus. Mostly Chest X-Ray (CXR) is used to detect the infection, but due to limitation of existing equipment, bandwidth, storage space we obtain low quality images. Spatial resolution of medical images is reduced due to image acquisition time, low radiation dose. Quality in medical images plays a major role for clinical diagnosis of disease in deep learning. There is no doubt that noise, low resolution and annotations in chest images are major constraint to the performance of deep learning. Researchers used famous image enhancement algorithms: Histogram equalization (HE), Contrast-limited Adaptive Histogram Equalization (CLAHE), De-noising, Discrete Wavelet Transform (DWT), Gamma Correction (GC), but it is still a challenging task to improve features in images. Computer vision and Super resolution are growing fields of deep learning. Super resolution is also feasible for mono chromatic medical images, which improve the region of interest. Multiple low-resolution images mix with high resolution and then reconstruct a target input image to high quality image by using Super Convolution Neural Network (SRCNN). The objective evaluation based on pixel difference-based PSNR and Human visual system SSIM metric are used for quality measurement. In this study we achieve effective value of PSNR (40 to 43 dB) by considering 30 images of different category (normal, viral or bacterial pneumonia) and SSIM value varies from 97% to 98%. The experiment shows that image quality of CXR is increased by SRCNN, and then high qualitative images will be used for further classification, so that significant parameter of accuracy will be finding in diagnosis of disease in deep learning

Sinogram Restoration for Low-Dosed X-Ray Computed Tomography Using Fractional-Order Perona-Malik Diffusion

Existing integer-order Nonlinear Anisotropic Diffusion (NAD) used in noise suppressing will produce undesirable staircase effect or speckle effect. In this paper, we propose a new scheme, named Fractal-order Perona-Malik Diffusion (FPMD), which replaces the integer-order derivative of the Perona-Malik (PM) Diffusion with the fractional-order derivative using G-L fractional derivative. FPMD, which is a interpolation between integer-order Nonlinear Anisotropic Diffusion (NAD) and fourth-order partial differential equations, provides a more flexible way to balance the noise reducing and anatomical details preserving. Smoothing results for phantoms and real sinograms show that FPMD with suitable parameters can suppress the staircase effects and speckle effects efficiently. In addition, FPMD also has a good performance in visual quality and root mean square errors (RMSE)

Adaptive spatial-temporal filtering applied to x-ray fluoroscopy angiography

Adaptive filtering of temporally varying X-ray image sequences acquired during endovascular interventions can improve the visual tracking of catheters by radiologists. Existing techniques blur the important parts of image sequences, such as catheter tips, anatomical structures and organs; and they may introduce trailing artifacts. To address this concern, an adaptive filtering process is presented to apply temporal filtering in regions without motion and spatial filtering in regions with motion. The adaptive filtering process is a multi-step procedure. First a normalized motion mask that describes the differences between two successive frames is generated. Secondly each frame is spatially filtered using the specific motion mask to specify different types of filtering in each region. Third an IIR filter is then used to combine the spatially filtered image with the previous output image; the motion mask thus serves as a weighted input mask to determine how much spatial and temporal filtering should be applied. This method results in improving both the stationary and moving fields. The visibility of static anatomical structures and organs increases, while the motion of the catheter tip and motion of anatomical structures and organs remain unblurred and visible during interventional procedures

A Comprehensive Review of Image Restoration and Noise Reduction Techniques

Images play a crucial role in modern life and find applications in diverse fields, ranging from preserving memories to conducting scientific research. However, images often suffer from various forms of degradation such as blur, noise, and contrast loss. These degradations make images difficult to interpret, reduce their visual quality, and limit their practical applications. To overcome these challenges, image restoration and noise reduction techniques have been developed to recover degraded images and enhance their quality. These techniques have gained significant importance in recent years, especially with the increasing use of digital imaging in various fields such as medical imaging, surveillance, satellite imaging, and many others. This paper presents a comprehensive review of image restoration and noise reduction techniques, encompassing spatial and frequency domain methods, and deep learning-based techniques. The paper also discusses the evaluation metrics utilized to assess the effectiveness of these techniques and explores future research directions in this field. The primary objective of this paper is to offer a comprehensive understanding of the concepts and methods involved in image restoration and noise reduction

A Novel Hybrid CNN Denoising Technique (HDCNN) for Image Denoising with Improved Performance

Photo denoising has been tackled by deep convolutional neural networks (CNNs) with powerful learning capabilities. Unfortunately, some CNNs perform badly on complex displays because they only train one deep network for their image blurring models. We recommend a hybrid CNN denoising technique (HDCNN) to address this problem. An HDCNN consists of a dilated interfere with, a RepVGG block, an attribute sharpening interferes with, as well as one inversion. To gather more context data, DB incorporates a stretched convolution, data sequential normalization (BN), shared convergence, and the activating function called the ReLU. Convolution, BN, and reLU are combined in parallel by RVB to obtain complimentary width characteristics. The RVB's refining characteristics are used to refine FB, which is then utilized to collect more precise data. To create a crisp image, a single convolution works in conjunction with a residual learning process. These crucial elements enable the HDCNN to carry out visual denoising efficiently. The suggested HDCNN has a good denoising performance in open data sets, according to experiments

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table