scholarly journals Phase- and GVF-Based Level Set Segmentation of Ultrasonic Breast Tumors

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Liang Gao ◽  
Xiaoyun Liu ◽  
Wufan Chen
Keyword(s):  
Level Set ◽  
Breast Tumor ◽  
Active Contour Model ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Good Convergence ◽  
Capture Range ◽  
Edge Based ◽  
Concave Boundaries

Automatically extracting breast tumor boundaries in ultrasound images is a difficult task due to the speckle noise, the low image contrast, the variance in shapes, and the local changes of image intensity. In this paper, an improved edge-based active contour model in a variational level set formulation is proposed for semi-automatically capturing ultrasonic breast tumor boundaries. First, we apply the phase asymmetry approach to enhance the edges, and then we define a new edge stopping function, which can increase the robustness to the intensity inhomogeneities. To extend the capture range of the method and provide good convergence to boundary concavities, we use the phase information to obtain an improved edge map, which can be used to calculate the gradient vector flow (GVF). Combining the edge stopping term and the improved GVF in the level set framework, the proposed method can robustly cope with noise, and it can extract the low contrast and/or concave boundaries well. Experiments on breast ultrasound images show that the proposed method outperforms the state-of-art methods.

Modified Distance Regularized Level Set Segmentation Based Analysis for Kidney Stone Detection

2015 ◽  
Vol 2 (2) ◽  
pp. 24-41 ◽  
Author(s):  
K. Viswanath ◽  
R. Gunasundari
Keyword(s):  
Energy Level ◽  
Level Set ◽  
High Speed ◽  
Gabor Filter ◽  
Energy Levels ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Level Set Function ◽  
Level Set Segmentation

The abnormalities of the kidney can be identified by ultrasound imaging. The kidney may have structural abnormalities like kidney swelling, change in its position and appearance. Kidney abnormality may also arise due to the formation of stones, cysts, cancerous cells, congenital anomalies, blockage of urine etc. For surgical operations it is very important to identify the exact and accurate location of stone in the kidney. The ultrasound images are of low contrast and contain speckle noise. This makes the detection of kidney abnormalities rather challenging task. Thus preprocessing of ultrasound images is carried out to remove speckle noise. In preprocessing, first image restoration is done to reduce speckle noise then it is applied to Gabor filter for smoothening. Next the resultant image is enhanced using histogram equalization. The preprocessed ultrasound image is segmented using distance regularized level set segmentation (DR-LSS), since it yields better results. It uses a two-step splitting methods to iteratively solve the DR-LSS equation, first step is iterating LSS equation, and then solving the Sign distance equation. The second step is to regularize the level set function which is the obtained from first step for better stability. The DR is included for LSS for eliminating of anti-leakages on image boundary. The DR-LSS does not require any expensive re-initialization and it is very high speed of operation. The RD-LSS results are compared with distance regularized level set evolution DRLSE1, DRLSE2 and DRLSE3. Extracted region of the kidney after segmentation is applied to Symlets (Sym12), Biorthogonal (bio3.7, bio3.9 & bio4.4) and Daubechies (Db12) lifting scheme wavelet subbands to extract energy levels. These energy level gives an indication about presence of stone in that particular location which significantly vary from that of normal energy level. These energy levels are trained by Multilayer Perceptron (MLP) and Back Propagation (BP) ANN to identify the type of stone with an accuracy of 98.6%.

AUTOMATIC 3D PROSTATE SURFACE DETECTION FROM TRUS WITH LEVEL SETS

2004 ◽  
Vol 04 (03) ◽  
pp. 385-403 ◽  
Author(s):  
FAN SHAO ◽  
KECK VOON LING ◽  
WAN SING NG
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Level Sets ◽  
Transrectal Ultrasound ◽  
User Interaction ◽  
Speckle Noise ◽  
Boundary Detection ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Weak Boundaries

Prostate boundary detection from ultrasound images plays an important role in prostate disease diagnoses and treatments. However, due to the low contrast, speckle noise and shadowing in ultrasound images, this still remains a difficult task. Currently, prostate boundary detection is performed manually, which is arduous and heavily user dependent. A possible solution is to improve the efficiency by automating the boundary detection process with minimal manual involvement. This paper presents a new approach based on the level set method to automatically detect the prostate surface from 3D transrectal ultrasound images. The user interaction in the initialization procedure is relieved by automatically putting the centroid of the initial zero level sets close to the image center. Region information, instead of the image gradient, is integrated into the level set method to remedy the "boundary leaking" problem caused by gaps or weak boundaries. Moreover, to increase the accuracy and robustness, knowledge-based features, such as expected shape (kidney-like) and ultrasound appearance of the prostate (looking from within the gland, the intensities are transitions from dark to light), are also incorporated into the model. The proposed method is applied to eight 3D TRUS images and the results have shown its effectiveness.

Modified Distance Regularized Level Set Segmentation Based Analysis for Kidney Stone Detection

2017 ◽  
pp. 693-710
Author(s):  
K. Viswanath ◽  
R. Gunasundari
Keyword(s):  
Energy Level ◽  
Level Set ◽  
High Speed ◽  
Gabor Filter ◽  
Energy Levels ◽  
Back Propagation ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Level Set Segmentation

The abnormalities of the kidney can be identified by ultrasound imaging. The kidney may have structural abnormalities like kidney swelling, change in its position and appearance. Kidney abnormality may also arise due to the formation of stones, cysts, cancerous cells, congenital anomalies, blockage of urine etc. For surgical operations it is very important to identify the exact and accurate location of stone in the kidney. The ultrasound images are of low contrast and contain speckle noise. This makes the detection of kidney abnormalities rather challenging task. Thus preprocessing of ultrasound images is carried out to remove speckle noise. In preprocessing, first image restoration is done to reduce speckle noise then it is applied to Gabor filter for smoothening. Next the resultant image is enhanced using histogram equalization. The preprocessed ultrasound image is segmented using distance regularized level set segmentation (DR-LSS), since it yields better results. It uses a two-step splitting methods to iteratively solve the DR-LSS equation, first step is iterating LSS equation, and then solving the Sign distance equation. The second step is to regularize the level set function which is the obtained from first step for better stability. The DR is included for LSS for eliminating of anti-leakages on image boundary. The DR-LSS does not require any expensive re-initialization and it is very high speed of operation. The RD-LSS results are compared with distance regularized level set evolution DRLSE1, DRLSE2 and DRLSE3. Extracted region of the kidney after segmentation is applied to Symlets (Sym12), Biorthogonal (bio3.7, bio3.9 & bio4.4) and Daubechies (Db12) lifting scheme wavelet subbands to extract energy levels. These energy level gives an indication about presence of stone in that particular location which significantly vary from that of normal energy level. These energy levels are trained by Multilayer Perceptron (MLP) and Back Propagation (BP) ANN to identify the type of stone with an accuracy of 98.6%.

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

scholarly journals Weighted area constraints-based breast lesion segmentation in ultrasound image analysis

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Qianting Ma ◽  
Tieyong Zeng ◽  
Dexing Kong ◽  
Jianwei Zhang
Keyword(s):  
Level Set ◽  
Domain Knowledge ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Breast Ultrasound ◽  
Nonlinear Constraint ◽  
Low Contrast ◽  
Anisotropic Diffusion Filter ◽  
Initial Segmentation ◽  
Speed Up

<p style='text-indent:20px;'>Breast ultrasound segmentation is a challenging task in practice due to speckle noise, low contrast and blurry boundaries. Although numerous methods have been developed to solve this problem, most of them can not produce a satisfying result due to uncertainty of the segmented region without specialized domain knowledge. In this paper, we propose a novel breast ultrasound image segmentation method that incorporates weighted area constraints using level set representations. Specifically, we first use speckle reducing anisotropic diffusion filter to suppress speckle noise, and apply the Grabcut on them to provide an initial segmentation result. In order to refine the resulting image mask, we propose a weighted area constraints-based level set formulation (WACLSF) to extract a more accurate tumor boundary. The major contribution of this paper is the introduction of a simple nonlinear constraint for the regularization of probability scores from a classifier, which can speed up the motion of zero level set to move to a desired boundary. Comparisons with other state-of-the-art methods, such as FCN-AlexNet and U-Net, show the advantages of our proposed WACLSF-based strategy in terms of visual view and accuracy.</p>

scholarly journals Active Contour Model for Ultrasound Images with Rayleigh Distribution

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Guodong Wang ◽  
Qian Dong ◽  
Zhenkuan Pan ◽  
Ximei Zhao ◽  
Jinbao Yang ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Speckle Noise ◽  
Rayleigh Distribution ◽  
Ultrasound Images ◽  
Fast Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Multiplicative Noises ◽  
Contour Model

Ultrasound images are often corrupted by multiplicative noises with Rayleigh distribution. The noises are strong and often called speckle noise, so segmentation is a hard work with this kind of noises. In this paper, we incorporate multiplicative noise removing model into active contour model for ultrasound images segmentation. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. Our model can segment the noisy ultrasound images very well. Finally, a fast method called Split-Bregman method is used for the easy implementation of segmentation. Experiments on a variety of synthetic and real ultrasound images validate the performance of our method.

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 Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

2017 ◽  
Vol 17 (4) ◽  
pp. 165-182 ◽  
Author(s):  
Abdallah Azizi ◽  
Kaouther Elkourd ◽  
Zineb Azizi
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Local Binary Pattern ◽  
Active Contour Model ◽  
Intensity Variation ◽  
Level Set Function ◽  
Contour Model ◽  
Local Intensity ◽  
Edge Based ◽  
Set Function

AbstractEdge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

Speckle Noise Diffusion in Knee Articular Cartilage Ultrasound Images

2020 ◽  
Vol 16 (6) ◽  
pp. 739-751
Author(s):  
Muhammad Ali Shoaib ◽  
Md Belayet Hossain ◽  
Yan Chai Hum ◽  
Joon Huang Chuah ◽  
Maheza Irna Mohd Salim ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
Contrast Ratio ◽  
Speckle Noise ◽  
Absolute Error ◽  
Cartilage Injury ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Reduction Techniques ◽  
Articular Cartilage Injury

Background: Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e., low contrast ratio and presence of speckle noise. Aims: A variation of anisotropic diffusion is proposed that can reduce speckle noise without compromising the image quality of the edges and other important details. Methods: For this technique, four gradient thresholds were adopted instead of one. A new diffusivity function that preserves the edge of the resultant image is also proposed. To automatically terminate the iterative procedures, the Mean Absolute Error as its stopping criterion was implemented. Results: Numerical results obtained by simulations unanimously indicate that the proposed method outperforms conventional speckle reduction techniques. Nevertheless, this preliminary study has been conducted based on a small number of asymptomatic subjects. Conclusion: Future work must investigate the feasibility of this method in a large cohort and its clinical validity through testing subjects with a symptomatic cartilage injury.

Improved Adaptive Wavelet Thresholding for Effective Speckle Noise Reduction in Low Contrast Medical Images

2019 ◽  
Vol 28 (10) ◽  
pp. 1950176 ◽  
Author(s):  
P. Sreelatha ◽  
M. Ezhilarasi
Keyword(s):  
Contrast Enhancement ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Gamma Correction ◽  
Significant Information ◽  
Adaptive Wavelet ◽  
Low Contrast ◽  
Image Contrast Enhancement

Informative images endure from poor contrast and noise during image acquisition. Significant information retrieval necessitates image contrast enhancement and removal of noise as a prerequisite before any further processing can be done. Dominant applications with low contrast images affected by speckle noise are medical ultrasound images. The objective of this work is to improve the effectiveness of the preprocessing stage in medical ultrasound images by enhancing the image while retaining its structural characteristics. For image enhancement, this work proposes to develop an automatic contrast enhancement technique using cumulative histogram equalization and gamma correction based on the image. For noise removal, this work proposes an algorithm Gamma Correction with Exponentially Adaptive Threshold (GCEAT) which suggests the use of GC for contrast enhancement along with a new wavelet-based adaptive soft thresholding technique for noise removal. The proposed GCEAT-based image de-noising is validated with other enhancement and noise removal techniques. Experimental results with low contrast synthetic and actual ultrasound images show that the suggested proposed system performs better than existing contrast enhancement techniques. Encouraging results were obtained with medical ultrasound images in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Average Intensity (AI).

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