Design and analysis performance of kidney stone detection from ultrasound image by level set segmentation and ANN classification

Viswanath, K.; Gunasundari, R.

doi:10.1109/icacci.2014.6968485

Cited by 44 publications

(19 citation statements)

References 16 publications

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“…The ANN is trained with normal kidney image and classified image input for normal or abnormal conditions by considering extracted energy levels from wavelets filters. The developed system is examined for different kidney images from the database and the results are effective in classifying the types of stone successfully with the accuracy of 98.8% [23]. Thus this system can be readily utilized in the hospitals for patients with abnormality in kidney.…”

Section: Conclusion and Scope For Future Workmentioning

confidence: 96%

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

Viswanath

Gunasundari

2015

VLSI Design

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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.

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Section: Conclusion and Scope For Future Workmentioning

confidence: 96%

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

Viswanath

Gunasundari

2015

VLSI Design

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“…In the second step is applied level set segmentation based on Symlets, Biorthogonal, Daubechies wavelet. In the last step is applied ANN classification for identifying kidney stones [8].…”

Section: Related Workmentioning

confidence: 99%

Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours

Vilímek¹,

Kubíček²,

Kloudova³

et al. 2019

Acta Mechanica Slov.

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Kidney stones (nephrolithiasis) are among the most common kidney diseases. They are solid stones that arise from minerals dissolved in urine. The treatment of kidney stones depends primarily on the position, size and composition of the stones, as well as on the general health condition of the patient. However, early diagnosis is quite complicated. In this paper, we propose a fully automatic hybrid method for identification and features extraction of the kidney stones. Specifically, model is based on the multiregional segmentation and approximation of the kidney stone area. Methods consequently use concept of the active contours which are focused on extraction of the geometrical features. The method remarkably allowing for an objective monitoring and classification of the kidney stones. These results may play a role to overcome conventionally used clinical procedures where clinicians mark kidney stones manually, without software feedback.

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“…An assortment of purposes behind expanded stone pervasiveness has been postulated, with the basic supposition that natural variables, instead of hereditary ones, are capable, on account of the moderately brief time course over which these epidemiologic patterns have been observed [1]. Renal calculus disease is world's problem where in any delay in the detection can lead to loss of a life [2].Ultrasonic (US) images are used for analyzing and locating renal diseases [3]. Different pattern recognition and noise removal methods are used to identify and locate diseases [4].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Optimized Probabilistic Neural Network Based Kidney Stone Detection and Segmentation over Ultrasound Images

P*¹,

Rao.V²,

Rao.B³

2019

IJRTE

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Abstract: Locating renal calculus in the ultrasound image is a demanding requirement in the field of medical imaging. For accurate detection of kidney stone, in this paper, optimal recurrent neural network (OPNN) is adopted. The proposed work undergoes pre-processing, feature extraction, classification, and segmentation. Initially, the noise present in input images is removed with the median filter because noises impact the accuracy of the classification. Then, compute features of this image. In the classification stage, features are used to classify defects through optimal probabilistic NeuralNetwork (OPNN). OPNN is a combination of PNN and spider monkey optimization (SMO). The parameter of PNN is optimized with the help of SMO. Then, the stone region from the abnormal image is segmented using probabilistic fuzzy c-means clustering (PFCM). The proposed methodology performance can be analyzed by usingSensitivity, Accuracy, and Specificity.

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Design and analysis performance of kidney stone detection from ultrasound image by level set segmentation and ANN classification

Cited by 44 publications

References 16 publications

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

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

Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours

An Efficient Optimized Probabilistic Neural Network Based Kidney Stone Detection and Segmentation over Ultrasound Images

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