DIGITAL IMAGE PROCESSING IN ULTRASOUND IMAGES

Image processing plays a vital role in the development of medical industry especially ultrasound medical images. Image acquisition is carried out using ultrasound equipments like transducer, scanner, and CPU and display device. There are mainly four modes of ultrasound imaging A - mode, M - mode, B - mode and Doppler - mode. Ultrasound imaging plays crucial role in medical imaging due to its non - invasive nature, low cost and capability of forming real time imaging. Modern ultrasound systems are signal processing intensive. Advanced techniques of signal processing are used to provide better image quality and higher diagnostic valu

Pseudocolouring Enhancement Processing Of Ovarian Ultrasound Images.

Image processing may be employed on ovarian ultrasound images to assist doctors in diagnostic analysis

Ovaidan Ultrasound Image Enhancement By Pseudocolouring.

Image processing may be employed on ovarian ultrasound images to assist doctors in diagnostic analysis. The gray levels of ovarian images are usually concentrated at the zero end of the spectrum ,making the image too low in contrast and too dark for the naked eye. This paper examines the effectiveness in displaying gray level ultrasound images as colour images and proposes a pseudocolouring approach for enhancing features in ultrasound ovarian image, which allows easy discrimination Of texture information

Comparative analysis of image enhancement techniques for uterine fibroid ultrasound

Background: The Ultrasound image is a vital diagnostic tool in the preliminary clinical assessment of many diseases, especially in Obstetrics and Gynecology. However, poor ultrasound image quality often leads to the inaccurate diagnosis of diseases such as uterine fibroids. Many researchers have proposed various methods for improving ultrasound image quality. Objective: To explore by comparison of four image enhancement techniques, the best approach for the enhancement of uterine fibroid images towards achieving better diagnosis and proper management of the disease..Methodology: The study assessed and compared the performance of four (4) different image enhancement techniques namely; Contrast stretching, Gamma correction, Histogram equalization(HE) and Contrast limited adaptive histogram equalization (CLAHE) on uterine fibroid ultrasound image Twenty (20) Ultrasound images from thedatabasewere downloaded and processed in MATLAB (2015a version) using image processing toolbox. Based on histogram distribution and statistical features (Mean, Standard Deviation and Entropy), the enhanced images were evaluated and compared. Results: The results show that Contrast stretching performed better based on Histogram distribution while CLAHE shows superior performance on Statistical featuresConclusion: Contrast stretching and Contrast limited adaptive histogram equalization (CLAHE)have demostrated good performance in enhancement of uterine fibroid ultrasound ima

Implementation of Cost Efficient Image Enhancement Technique Reduce Speckle in Ultrasound Images

Speckle is a granular multiplicative noise that reduces the resolution and contrast of the image there by degrading the diagnostic accuracy of the Ultrasound image. Speckle reduction technique has to be followed to enhance the quality of ultrasound image [3].Speckle noise occurs in all coherent imaging systems, such as ultrasound images. The speckle noise in ultrasound images is often considered as undesirable and has a negative impact on clinical practitioners for diagnosis. Because of the signal-dependent nature of the speckle intensity, speckle noise in ultrasound imaging requires specific handling. So, any ultrasound speckle de-noising method must be designed in such a way that the speckle noise be suppressed without smearing the edges. In other words, any speckle de-noising method must preserve both the edges and structural details of the image and its quality [8].Digital image enhancement techniques are to improving the visual quality of images. Main objective of image enhancement is to process an image so that result is more suitable than original image for specific application. This paper presents real time hardware image enhancement techniques using field programmable gate array (FPGA) [10].It presents architecture for filters pixel by pixel and regions filters for image processing using Xilinx System Generator (XSG). This architecture offer an alternative through a graphical user interface that combines MATLAB, Simulink and XSG and explore important aspects concerned to hardware implementation

Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis.

BACKGROUND: Staging diagnosis of liver fibrosis is a prerequisite for timely diagnosis and therapy in patients with chronic hepatitis B. In recent years, ultrasound elastography has become an important method for clinical noninvasive assessment of liver fibrosis stage, but its diagnostic value for early liver fibrosis still needs to be further improved. In this study, the texture analysis was carried out on the basis of two dimensional shear wave elastography (2D-SWE), and the feasibility of 2D-SWE plus texture analysis in the diagnosis of early liver fibrosis was discussed. AIM: To assess the diagnostic value of 2D-SWE combined with textural analysis in liver fibrosis staging. METHODS: This study recruited 46 patients with chronic hepatitis B. Patients underwent 2D-SWE and texture analysis; Young\u27s modulus values and textural patterns were obtained, respectively. Textural pattern was analyzed with regard to contrast, correlation, angular second moment (ASM), and homogeneity. Pathological results of biopsy specimens were the gold standard; comparison and assessment of the diagnosis efficiency were conducted for 2D-SWE, texture analysis and their combination. RESULTS: 2D-SWE displayed diagnosis efficiency in early fibrosis, significant fibrosis, severe fibrosis, and early cirrhosis (AUC \u3e 0.7, P \u3c 0.05) with respective AUC values of 0.823 (0.678-0.921), 0.808 (0.662-0.911), 0.920 (0.798-0.980), and 0.855 (0.716-0.943). Contrast and homogeneity displayed independent diagnosis efficiency in liver fibrosis stage (AUC \u3e 0.7, P \u3c 0.05), whereas correlation and ASM showed limited values. AUC of contrast and homogeneity were respectively 0.906 (0.779-0.973), 0.835 (0.693-0.930), 0.807 (0.660-0.910) and 0.925 (0.805-0.983), 0.789 (0.639-0.897), 0.736 (0.582-0.858), 0.705 (0.549-0.883) and 0.798 (0.650-0.904) in four liver fibrosis stages, which exhibited equivalence to 2D-SWE in diagnostic efficiency (P \u3e 0.05). Combined diagnosis (PRE) displayed diagnostic efficiency (AUC \u3e 0.7, P \u3c 0.01) for all fibrosis stages with respective AUC of 0.952 (0.841-0.994), 0.896 (0.766-0.967), 0.978 (0.881-0.999), 0.947 (0.835-0.992). The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis (P \u3c 0.05), whereas no significant differences were observed in other comparisons (P \u3e 0.05). CONCLUSION: Texture analysis was capable of diagnosing liver fibrosis stage, combined diagnosis had obvious advantages in early liver fibrosis, liver fibrosis stage might be related to the hepatic tissue hardness distribution

Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering

Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic procedures. However, the diagnosis by ultrasound relies heavily on the quality of images assessed manually by sonographers, which diminishes the objectivity of the diagnosis and makes it operator-dependent. The supervised learning-based methods for automated quality assessment require manually annotated datasets, which are highly labour-intensive to acquire. These ultrasound images are low in quality and suffer from noisy annotations caused by inter-observer perceptual variations, which hampers learning efficiency. We propose an UnSupervised UltraSound image Quality assessment Network, US2QNet, that eliminates the burden and uncertainty of manual annotations. US2QNet uses the variational autoencoder embedded with the three modules, pre-processing, clustering and post-processing, to jointly enhance, extract, cluster and visualize the quality feature representation of ultrasound images. The pre-processing module uses filtering of images to point the network's attention towards salient quality features, rather than getting distracted by noise. Post-processing is proposed for visualizing the clusters of feature representations in 2D space. We validated the proposed framework for quality assessment of the urinary bladder ultrasound images. The proposed framework achieved 78% accuracy and superior performance to state-of-the-art clustering methods.Comment: Accepted in IEEE International Conference on Robotics and Automation (ICRA) 202

Curvelet Denoising with Improved Thresholds for Application on Ultrasound Images

In medical image processing, image denoising has become a very essential exercise all through the diagnose. Negotiation between the preservation of useful diagnostic information and noise suppression must be treasured in medical images. In case of ultrasonic images a special type of acoustic noise, technically known as speckle noise, is the major factor of image quality degradation. Many denoising techniques have been proposed for effective suppression of speckle noise. Removing noise from the original image or signal is still a challenging problem for researchers. In this paper, a Curvelet transform based denoising with improved thresholds is proposed for ultrasound images