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

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%.

Download Full-text

Analysis and Implementation of Kidney Stone Detection by Reaction Diffusion Level Set Segmentation Using Xilinx System Generator on FPGA

VLSI Design ◽

10.1155/2015/581961 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Kalannagari Viswanath ◽

Ramalingam Gunasundari

Keyword(s):

Level Set ◽

Ultrasound Imaging ◽

Kidney Stone ◽

Gabor Filter ◽

Imaging Techniques ◽

Energy Levels ◽

Speckle Noise ◽

System Generator ◽

Level Set Segmentation ◽

Xilinx System Generator

Ultrasound imaging is one of the available imaging techniques used for diagnosis of kidney abnormalities, which may be like change in shape and position and swelling of limb; there are also other Kidney abnormalities such as formation of stones, cysts, blockage of urine, congenital anomalies, and cancerous cells. During surgical processes it is vital to recognize the true and precise location of kidney stone. The detection of kidney stones using ultrasound imaging is a highly challenging task as they are of low contrast and contain speckle noise. This challenge is overcome by employing suitable image processing techniques. The ultrasound image is first preprocessed to get rid of speckle noise using the image restoration process. The restored image is smoothened using Gabor filter and the subsequent image is enhanced by histogram equalization. The preprocessed image is achieved with level set segmentation to detect the stone region. Segmentation process is employed twice for getting better results; first to segment kidney portion and then to segment the stone portion, respectively. In this work, the level set segmentation uses two terms, namely, momentum and resilient propagation (Rprop) to detect the stone portion. After segmentation, the extracted region of the kidney stone is given to Symlets, Biorthogonal (bio3.7, bio3.9, and bio4.4), and Daubechies lifting scheme wavelet subbands to extract energy levels. These energy levels provide evidence about presence of stone, by comparing them with that of the normal energy levels. They are trained by multilayer perceptron (MLP) and back propagation (BP) ANN to classify and its type of stone with an accuracy of 98.8%. The prosed work is designed and real time is implemented on both Filed Programmable Gate Array Vertex-2Pro FPGA using Xilinx System Generator (XSG) Verilog and Matlab 2012a.

Download Full-text

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

Journal of Applied Mathematics ◽

10.1155/2012/810805 ◽

2012 ◽

Vol 2012 ◽

pp. 1-22 ◽

Cited By ~ 7

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.

Download Full-text

AUTOMATIC 3D PROSTATE SURFACE DETECTION FROM TRUS WITH LEVEL SETS

International Journal of Image and Graphics ◽

10.1142/s0219467804001476 ◽

2004 ◽

Vol 04 (03) ◽

pp. 385-403 ◽

Cited By ~ 7

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.

Download Full-text

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

Inverse Problems and Imaging ◽

10.3934/ipi.2021057 ◽

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>

Download Full-text

Evaluation of a level set segmentation method for cardiac ultrasound images

10.1117/12.770392 ◽

2008 ◽

Cited By ~ 1

Author(s):

Yong Yue ◽

Hemant D. Tagare

Keyword(s):

Level Set ◽

Ultrasound Images ◽

Cardiac Ultrasound ◽

Segmentation Method ◽

Level Set Segmentation

Download Full-text

A Level Set Segmentation Method of the Four Heart Cavities in Pediatric Ultrasound Images

Lecture Notes in Computer Science - Image Analysis and Recognition ◽

10.1007/978-3-642-13775-4_11 ◽

2010 ◽

pp. 99-107 ◽

Cited By ~ 9

Author(s):

Sofia G. Antunes ◽

José Silvestre Silva ◽

Jaime B. Santos

Keyword(s):

Level Set ◽

Ultrasound Images ◽

Segmentation Method ◽

Level Set Segmentation

Download Full-text

Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images.

Reproductive Biology and Endocrinology ◽

10.1186/1477-7827-6-33 ◽

2008 ◽

Vol 6 (1) ◽

pp. 33 ◽

Cited By ~ 2

Author(s):

Brennan J Russnel ◽

Roger A Pierson ◽

Jaswant Singh ◽

Gregg P Adams ◽

Mark G Eramian

Keyword(s):

Level Set ◽

Ex Situ ◽

Corpora Lutea ◽

Ultrasound Images ◽

Level Set Segmentation

Download Full-text

FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2019.p1073 ◽

2019 ◽

Vol 23 (6) ◽

pp. 1073-1079

Author(s):

Zhongming Luo ◽

Yu Zhang ◽

Zixuan Zhou ◽

Xuan Bi ◽

Haibin Wu ◽

...

Keyword(s):

Image Segmentation ◽

Level Set ◽

Clustering Algorithm ◽

Evolution Equations ◽

High Efficiency ◽

Level Set Function ◽

Level Set Segmentation ◽

Segmentation Algorithms ◽

Fcm Clustering ◽

Set Function

To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-treated micro-vessel images were segmented by the fuzzy c-means (FCM) clustering algorithm to obtain original contours of interesting areas in images. By the evolution equations of the improved level set function, accurate segmentation of microscopic micro-vessel images was realized. This method can effectively solve the problem of manual initialization of contours, avoid the sensitivity to initialization and improve the accuracy of level-set segmentation. The experiment results indicate that compared with traditional micro-vessel image segmentation algorithms, this algorithm is of high efficiency, good noise immunity and accurate image segmentation.

Download Full-text

EFFICIENT 3D PROSTATE SURFACE DETECTION FOR ULTRASOUND GUIDED ROBOTIC BIOPSY

International Journal of Humanoid Robotics ◽

10.1142/s0219843606000862 ◽

2006 ◽

Vol 03 (04) ◽

pp. 439-461 ◽

Cited By ~ 1

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.

Download Full-text