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

VLSI Design ◽  
2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
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.

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%.

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%.

Retraction Notice: Improved Level Set Segmentation Algorithm Based on Kernel Fuzzy Particles Swarm Optimization (KFPSO) Clustering for MRI Images

2018 ◽  
Vol 14 ◽  
Author(s):  
G. Elaiyaraja
Keyword(s):  
Medical Imaging ◽  
Level Set ◽  
Current Medical ◽  
Segmentation Algorithm ◽  
Scientific Publishing ◽  
Swarm Optimization ◽  
Original Source ◽  
Level Set Segmentation ◽  
Copyright Violation ◽  
Retraction Notice

The article entitled “Improved Level Set Segmentation Algorithm Based on Kernel Fuzzy Particles Swarm Optimization (KFPSO) Clustering for MRI Images”, by G. Elaiyaraja, P. Epsiba, N. Kumaratharan and G. Suresh, has been retracted. Kindly see Bentham Science Policy on Article retraction at the link given below: (https://www.benthamscience.com/journals/current-medical-imaging/author-guidelines/). This article has been retracted on the request of the Editor. The authors have plagiarized a paper that had already been published in the journal Current Medical Imaging (CMIM) (Formerly: Current Medical Imaging Reviews) 14(3), Page: 389-400. http://www.eurekaselect.com/149444. It is a pre-requisite for authors to declare explicitly that their work is original and has not been published elsewhere. Authors are advised to properly cite the original source to avoid plagiarism and copyright violation. As such this article represents a severe abuse of the scientific publishing system. Bentham Science Publishers takes a very strong view on this matter and apologizes to the readers of the journal for any inconvenience this may cause.

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