Contrast enhancement by multi-scale adaptive histogram equalization

An approach for contrast enhancement utilizing multi-scale analysis is introduced. Sub-band coefficients were modified by the method of adaptive histogram equalization. To achieve optimal contrast enhancement, the sizes of sub-regions were chosen with consideration to the support of the analysis filters. The enhanced images provided subtle details of tissues that are only visible with tedious contrast/brightness windowing methods currently used in clinical reading. We present results on chest CT data, which shows significant improvement over existing state-of-the-art methods: unsharp masking, adaptive histogram equalization (AHE), and the contrast limited adaptive histogram equalization (CLAHE). A systematic study on 109 clinical chest CT images by three radiologists suggests the promise of this method in terms of both interpretation time and diagnostic performance on different pathological cases. In addition, radiologists observed no noticeable artifacts or amplification of noise that usually appears in traditional adaptive histogram equalization and its variations

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Chest radiograph image enhancement with wavelet decomposition and morphological operations

Medical image processing algorithms significantly affect the precision ofdisease diagnostic process. This makes it crucial to improve the quality of a medical image with the goal to enhance perceivability of the points of interest in order to obtain accurate diagnosis of a patient.  Despite the reliance of various medical diagnostics on utilize X-rays, they are usually plagued by dark and low contrast properties. Sought-after  details in X-rays can only be accessed by means of digital image processing techniques, despite the fact that these techniques are far from being  perfect. In this paper, we implement a wavelet decomposition and reconstruction technique to enhance radiograph properties, some of which include contrast and noise, by using a series of morphological erosion and dilation to improve the visual quality of the chest radiographs for the detection of cancer nodules

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

Pleural Line Detection Enhancement in Lung Ultrasonography (LUS) Based on Morphological and Adaptive Structural 2D Filter

Lung ultrasonography (LUS) imaging has been used intensively to investigate and assess the lung’s various pathological conditions. A diagnostic system of lung abnormalities is developed to detect and localize the pleural line that can be viewed as the artifacts in LUS image. The continuous pleural line indicates one crucial pattern of a healthy lung. The regular repeated horizontal A-line marks this pattern with a fixed distance between the lines and ideally, produces a higher contrast in the lung image. This work proposes an image processing framework for enhancing pleural line detection in healt ung ultrasonography (LUS) imaging has been used intensively to investigate and assess the lung’s various pathological conditions. A diagnostic system of lung abnormalities is developed to detect and localize the pleural line that can be viewed as the artifacts in LUS image. The continuous pleural line indicates one crucial pattern of a healthy lung. The regular repeated horizontal A-line marks this pattern with a fixed distance between the lines and ideally, produces a higher contrast in the lung image. This work proposes an image processing framework for enhancing pleural line detection in healthy subjects and patients as an early stage of further lung image interpretations in pneumonia patients. The proposed image processing framework is based on a top-hat morphological grayscale 2D filter with a texture structure element and an adaptive structural 2D low pass filter. This framework is evaluated for open dataset video ultrasonography (USG) of Point-of-care ultrasound (POCUS) to enhance the pleural line detection for typical video LUS acquired using a linear and a convex transducer. hy subjects and patients as an early stage of further lung image interpretations in pneumonia patients. The proposed image processing framework is based on a top-hat morphological grayscale 2D filter with a texture structure element and an adaptive structural 2D low pass filter. This framework is evaluated for open dataset video ultrasonography (USG) of Point-of-care ultrasound (POCUS) to enhance the pleural line detection for typical video LUS acquired using a linear and a convex transducer

Cardiac Motion Analysis Based on Optical Flow on Real-Time Three-Dimensional Ultrasound Data

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. Finite element surfaces were fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency with less than 10% difference between manual tracing and optical flow estimation on 73% of the entire surface. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets. A displacement field was computed from the optical flow output, and a framework for computation of dynamic cardiac information is introduced. The method was applied to a clinical data set from a heart transplant patient and dynamic measurements agreed with known physiology as well as experimental results

Computer-assisted detection of lung cancer nudules in medical chest X-rays

Diagnostic medicine was revolutionized in 1895 with Rontgen's discovery of x-rays. X-ray photography has played a very prominent role in diagnostics of all kinds since then and continues to do so. It is true that more sophisticated and successful medical imaging systems are available. These include Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) and Positron Emission Tomography (PET). However, the hardware instalment and operation costs of these systems remain considerably higher than x-ray systems. Conventional x-ray photography also has the advantage of producing an image in significantly less time than MRI, CT and PET. X-ray photography is still used extensively, especially in third world countries. The routine diagnostic tool for chest complaints is the x-ray. Lung cancer may be diagnosed by the identification of a lung cancer nodule in a chest x-ray. The cure of lung cancer depends upon detection and diagnosis at an early stage. Presently the five-year survival rate of lung cancer patients is approximately 10%. If lung cancer can be detected when the tumour is still small and localized, the five-year survival rate increases to about 40%. However, currently only 20% of lung cancer cases are diagnosed at this early stage. Giger et al wrote that "detection and diagnosis of cancerous lung nodules in chest radiographs are among the most important and difficult tasks performed by radiologists"