scholarly journals A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images

2021 ◽  
Author(s):  
Rasa Vafaie
Keyword(s):  
Em Algorithm ◽  
Signal To Noise Ratio ◽  
Transrectal Ultrasound ◽  
Speckle Noise ◽  
Computational Time ◽  
Dice Similarity Coefficient ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Initial Model ◽  
Hybrid Segmentation

Segmentation of prostate boundaries in transrectal ultrasound (TRUS) images plays a great role in prostate cancer diagnosis. Due to the low signal to noise ratio and existence of the speckle noise in TRUS images, prostate image segmentation has proven to be an extremely difficult task. In this thesis report, a fast fully automated hybrid segmentation method based on probabilistic approaches is presented. First, the position of the initial model is automatically estimated using prostate boundary representative patterns. Next, the Expectation Maximization (EM) algorithm and Markov Random Field (MRF) theory are utilized in the deformation strategy to optimally fit the initial model on the prostate boundaries. A less computationally EM algorithm and a new surface smoothing technique are proposed to decrease the segmentation time. Successful experimental results with the average Dice Similarity Coefficient (DSC) value 93.9±2.7% and computational time around 9 seconds validate the algorithm.

scholarly journals A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images

2021 ◽  
Author(s):  
Rasa Vafaie
Keyword(s):  
Em Algorithm ◽  
Signal To Noise Ratio ◽  
Transrectal Ultrasound ◽  
Speckle Noise ◽  
Computational Time ◽  
Dice Similarity Coefficient ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Initial Model ◽  
Hybrid Segmentation

Segmentation of prostate boundaries in transrectal ultrasound (TRUS) images plays a great role in prostate cancer diagnosis. Due to the low signal to noise ratio and existence of the speckle noise in TRUS images, prostate image segmentation has proven to be an extremely difficult task. In this thesis report, a fast fully automated hybrid segmentation method based on probabilistic approaches is presented. First, the position of the initial model is automatically estimated using prostate boundary representative patterns. Next, the Expectation Maximization (EM) algorithm and Markov Random Field (MRF) theory are utilized in the deformation strategy to optimally fit the initial model on the prostate boundaries. A less computationally EM algorithm and a new surface smoothing technique are proposed to decrease the segmentation time. Successful experimental results with the average Dice Similarity Coefficient (DSC) value 93.9±2.7% and computational time around 9 seconds validate the algorithm.

scholarly journals MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

2020 ◽  
Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Speckle Noise ◽  
Noise Removal ◽  
General Purpose ◽  
Imaging Systems ◽  
Ultrasound Images ◽  
Frame Method ◽  
Probability Density Function Pdf ◽  
Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

EFFICIENT 3D PROSTATE SURFACE DETECTION FOR ULTRASOUND GUIDED ROBOTIC BIOPSY

2006 ◽  
Vol 03 (04) ◽  
pp. 439-461 ◽  
Author(s):  
FAN SHAO ◽  
KECK VOON LING ◽  
LOUIS PHEE ◽  
WAN SING NG ◽  
DI XIAO
Keyword(s):  
Current System ◽  
Transrectal Ultrasound ◽  
Speckle Noise ◽  
Practical Method ◽  
Ultrasound Images ◽  
Ultrasound Guided ◽  
Low Contrast ◽  
Surface Detection ◽  
Domain Specific Knowledge ◽  
Weak Boundaries

Prostate surface detection from ultrasound images plays a key role in our recently developed ultrasound guided robotic biopsy system. However, due to the low contrast, speckle noise and shadowing in ultrasound images, this still remains a difficult task. In the current system, a 3D prostate surface is reconstructed from a sequence of 2D outlines, which are performed manually. This is arduous and the results depend heavily on the user's expertise. This paper presents a new practical method, called Evolving Bubbles, based on the level set method to semi-automatically detect the prostate surface from transrectal ultrasound (TRUS) images. To produce good results, a few initial bubbles are simply specified by the user from five particular slices based on the prostate shape. When the initial bubbles evolve along their normal directions, they expand, shrink, merge and split, and finally are attracted to the desired prostate surface. Meanwhile, to remedy the boundary leaking problem caused by gaps or weak boundaries, domain specific knowledge of the prostate and statistical information are incorporated into the Evolving Bubbles. We apply the bubbles model to eight 3D and four stacks of 2D TRUS images and the results show its effectiveness.

scholarly journals A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals A Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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.

scholarly journals Performance Analysis of Various Filters for De-Speckling of Thyroid Ultrasound Images

2019 ◽  
Vol 9 (2) ◽  
pp. 910-917
Keyword(s):  
Mean Square Error ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Thyroid ultrasonography is the most common and extremely useful, safe, and cost effective way to image the thyroid gland and its pathology. However, an inherent characteristic of Ultrasound (US) imaging is the presence of multiplicative speckle noise. Speckle noise reduces the ability of an observer to distinguish fine details, make diagnosis more difficult. It limits the effective implementation of image analysis steps such as edge detection, segmentation and classification. The main objective of this study is to compare the performance of various spatial and frequency domain filters so as to identify efficient and optimum filter for de-speckling Thyroid US images. The performance of these filters is evaluated using the image quality assessment parameters Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE) and Root Mean Square Error (RMSE) for different speckle variance. Experimental work revealed that kuan filter resulted in higher PSNR, SNR, SSIM and least MSE, RMSE values compared to other filters

scholarly journals A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals Speckle and Rician Noise Removal from Medical Images and Ultrasound Images

2020 ◽  
Vol 8 (5) ◽  
pp. 1851-1854
Keyword(s):  
Noise Reduction ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Speckle Noise ◽  
Noise Removal ◽  
Difficult Problem ◽  
Ultrasound Images ◽  
Simple Method ◽  
Different Types

In medical images, medical images are corrupted by different types of noise. It is important to get a precise picture and accurately observe the correspondence. Removing noise from medical images has become a very difficult problem in the field of the medical image. The most well-known noise reduction method, which is usually based on the local statistics of medical images, is efficient because of the noise reduction of medical images. In paper, an efficient and simple method for noise reduction from medical images is presented. The paper proposes a filtering system to combine both the Median filter and Gaussian filter to remove the Speckle noise form Medical and Ultrasound images. The image quality is measured through statistical quantities: Peak signal to noise ratio (PSNR). Experimental results show that the proposed system removes Speckle noise from medical images.

Despeckling of Carotid Artery Ultrasound Images with a Calculus Approach

2019 ◽  
Vol 15 (4) ◽  
pp. 414-426 ◽  
Author(s):  
S. Latha ◽  
Dhanalakshmi Samiappan
Keyword(s):  
Carotid Artery ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Speckle Noise ◽  
Absolute Error ◽  
Block Matching ◽  
Ultrasound Images ◽  
Mean Square ◽  
Integral Calculus

<P>Background: Carotid artery images indicate any presence of plaque content, which may lead to atherosclerosis and stroke. Early identification of the disease is possible by taking B-mode ultrasound images in the carotid artery. Speckle is the inherent noise content in the ultrasound images, which essentially needs to be minimized. </P><P> Objective: The objective of the proposed method is to convert the multiplicative speckle noise into additive, after which the frequency transformations can be applied. </P><P> Method: The method uses simple differentiation and integral calculus and is named variable gradient summation. It differs from the conventional homomorphic filter, by preserving the edge features to a great extent and better denoising. The additive image is subjected to wavelet decomposition and further speckle filtering with three different filters Non Local Means (NLM), Vectorial Total Variation (VTV) and Block Matching and 3D filtering (BM3D) algorithms. By this approach, the components dependent on the image are identified and the unwanted noise content existing in the high frequency portion of the image is removed. </P><P> Results & Conclusion: Experiments conducted on a set of 300 B-mode ultrasound carotid artery images and the simulation results prove that the proposed method of denoising gives enhanced results as compared to the conventional process in terms of the performance evaluation methods like peak signal to noise ratio, mean square error, mean absolute error, root mean square error, structural similarity, quality factor, correlation and image enhancement factor.</P>

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