scholarly journals A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images

2010 ◽  
Vol 3 (1) ◽  
pp. 81 ◽  
Author(s):  
M. A. Yousuf ◽  
M. N. Nobi
Keyword(s):  
Image Processing ◽  
Magnetic Resonance ◽  
Noise Reduction ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Medical Image Processing ◽  
Experimental Results ◽  
Signal To Noise

In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images. In the proposed method median filter is modified by adding more features. Experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE). Experimental results of magnetic resonance (MR) image and ultrasound image demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms.Key words: Magnetic resonance image; Ultrasound image; PSNR; SNR; RMSE.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5544                J. Sci. Res. 3 (1), 81-89 (2011)

Grid enabled magnetic resonance scanners for near real-time medical image processing

2006 ◽  
Vol 66 (12) ◽  
pp. 1524-1533 ◽  
Author(s):  
Jason C. Crane ◽  
Forrest W. Crawford ◽  
Sarah J. Nelson
Keyword(s):  
Image Processing ◽  
Magnetic Resonance ◽  
Real Time ◽  
Medical Image ◽  
Medical Image Processing

The Location of Cancer Area Based on Region Growing Algorithm in Medical Image

2013 ◽  
Vol 760-762 ◽  
pp. 1552-1555 ◽  
Author(s):  
Jing Jing Wang ◽  
Xiao Wei Song ◽  
Mei Fang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Region Growing ◽  
Challenging Problem ◽  
Correct Judgment ◽  
Threshold Segmentation ◽  
Seed Points

Image segmentation in medical image processing has been extensively used which has also been applied in different fields of medicine to assist doctors to make the correct judgment and grasp the patient's condition. However, nowadays there are no image threshold segmentation techniques that can be applied to all of the medical images; so it has became a challenging problem. In this paper, it applies a method of identifying edge of the tissues and organs to recognize its contour, and then selects a number of seed points on the contour range to locate the cancer area by region growing. And finally, the result has demonstrated that this method can mostly locate the cancer area accurately.

scholarly journals IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING

2010 ◽  
Vol 13 (4) ◽  
pp. 20-27
Author(s):  
Linh Duy Tran ◽  
Linh Quang Huynh
Keyword(s):  
Image Processing ◽  
Diagnostic Imaging ◽  
3D Reconstruction ◽  
Programming Language ◽  
Medical Image ◽  
Medical Images ◽  
Rapid Development ◽  
Medical Image Processing ◽  
Matlab Programming

Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

scholarly journals Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus

2021 ◽  
Vol 11 (1) ◽  
pp. 399-410
Author(s):  
Kaitheri Thacharedath Dilna ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Signal To Noise ◽  
Statistical Measures ◽  
Noise Ratio ◽  
Scanned Images

Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.

Fuzzy-Based Medical Image Processing

2015 ◽  
pp. 666-681
Author(s):  
G. R. Sinha
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Hybrid Approach ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Clustering Methods ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Medical Image Processing (MIP) is a set of tools applied over medical images, which consists of several components such as image acquisition, enhancement, segmentation, restoration, etc. The most important component of MIP is medical image segmentation used in Computer-Aided Diagnosis (CAD) systems used for detection of abnormalities in medical images. This chapter presents an overview and the importance of soft computing techniques in solving the problems of medical imaging. The authors highlight the significance of fuzzy-based clustering and similar methods for MIP and its applications. Fuzzy C-Means Clustering Method (FCM) is found the most suitable method among existing clustering methods for medical images. FCM addresses the problem of over-segmentation and helps in improvement of diagnosis accuracy. Application of optimization tool causes the reduction of execution time. A comparison of fuzzy-based methods over conventional methods suggests that neuro-fuzzy system as hybrid approach is an efficient method for medical image analysis.

scholarly journals Medical Image Processing and Analysis Techniques for Detecting Giant Cell Arteritis

2021 ◽  
Author(s):  
Radwan Qasrawi ◽  
Diala Abu Al-Halawa ◽  
Omar Daraghmeh ◽  
Mohammad Hjouj ◽  
Rania Abu Seir
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Giant Cell Arteritis ◽  
Giant Cell ◽  
Case Studies ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Image Processing And Analysis ◽  
Detection And Diagnosis

Medical image segmentation and classification algorithms are commonly used in clinical applications. Several automatic and semiautomatic segmentation methods were used for extracting veins and arteries on transverse and longitudinal medical images. Recently, the use of medical image processing and analysis tools improved giant cell arteries (GCA) detection and diagnosis using patient specific medical imaging. In this chapter, we proposed several image processing and analysis algorithms for detecting and quantifying the GCA from patient medical images. The chapter introduced the connected threshold and region growing segmentation approaches on two case studies with temporal arteritis using ultrasound (US) and magnetic resonance imaging (MRI) imaging modalities extracted from the Radiopedia Dataset. The GCA detection procedure was developed using the 3D Slicer Medical Imaging Interaction software as a fast prototyping open-source framework. GCA detection passes through two main procedures: The pre-processing phase, in which we improve and enhances the quality of an image after removing the noise, irrelevant and unwanted parts of the scanned image by the use of filtering techniques, and contrast enhancement methods; and the processing phase which includes all the steps of processing, which are used for identification, segmentation, measurement, and quantification of GCA. The semi-automatic interaction is involved in the entire segmentation process for finding the segmentation parameters. The results of the two case studies show that the proposed approach managed to detect and quantify the GCA region of interest. Hence, the proposed algorithm is efficient to perform complete, and accurate extraction of temporal arteries. The proposed semi-automatic segmentation method can be used for studies focusing on three-dimensional visualization and volumetric quantification of Giant Cell Arteritis.

scholarly journals Noise Estimation using Kalman Filter in Image Restoration

2019 ◽  
Vol 8 (6S2) ◽  
pp. 888-891
Keyword(s):  
Image Processing ◽  
Kalman Filter ◽  
Image Restoration ◽  
Medical Image ◽  
Blind Deconvolution ◽  
Medical Images ◽  
Medical Image Processing ◽  
Noise Removal ◽  
Original Image ◽  
Degraded Image

Medical image processing plays a vital role in medical sciences from the past decades. Medical image processing becomes simple and useful with the advancement of image processing techniques. Medical images are used to observe the information related to inside the organs of human body. For better diagnoses and analysis of disease the image should be clear, noise free and more informative also. Usually medical images are corrupted by different noises in image acquisition and transmission process. The basic challenge in medical image processing is noise removal without losing diagnostic information. Image restoration is the one of the technique to recover the original image from the degraded image. In this paper, we are proposing a kalman filter to estimate the noise function from the degraded image and to reconstruct the original image. Here we are taking into account that the medical image was corrupted by the gaussian, speckle and salt & pepper noise. The simulation result infers that the proposed blind deconvolution method can be able to suppress the noise well and also preserve edge information without losing diagnostic data.

Generalizing the Evaluation of Medical Image Processing Tools by Use of Gabor Patterns

2009 ◽  
Vol 48 (04) ◽  
pp. 331-335 ◽  
Author(s):  
H. Strasburger ◽  
R. Rascher-Friesenhausen ◽  
J. Klein ◽  
B. Preim ◽  
D. Apelt
Keyword(s):  
Image Processing ◽  
Contrast Sensitivity ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Mammographic Images ◽  
Homogeneous Background ◽  
The Impact ◽  
Mcs Method ◽  
Observer Studies

Summary Objective: Tools for medical image processing are usually evaluated by observers with radiological experience and with complex tasks. For easing evaluation of filtering and enhancement tools, the observer’s task can be generalized. Methods: By describing aspects of the MCS method (Mammographic Contrast Sensitivity) we illustrate issues of selecting a metric for assessing visual performance, the observer’s task and the image material to be used, aiming at a generalization of the design of studies for the evaluation of medical image processing tools. Concerning the metric, we distinguish acuity from contrast sensitivity. With respect to the observer’s task, we distinguish tasks of discrimination from those at a higher level of recognition. Finally, we show the advantage of using medical images for evaluating image processing tools by comparing the results for measurements on homogeneous background and mammographic images. Results: The perceptual level of the observer’s task and the complexity of the used image material influences the outcome of observer studies, particularly also from crowding effects. The design of a study should minimize the impact of the observer’s experience on the outcome. This can be achieved by using non-anatomical, standardized perceptual targets like Gabor patterns, used in the context of medical images. Conclusions: Understanding the concepts of perception helps designing observer studies that are as complex as required, but at the same time as simple and general as possible. Performing an observer study may be simplified by a study design which does not require radiological experience of the observers, if the study aims at the evaluation of tools that shall support basic perception tasks, such as e.g. contrast enhancement.

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