Segmentation of renal calculi in ultrasound images

Sridhar, S.; Kumaravel, N.; Easwarakumar, K. S.

doi:10.1080/1463923021000054217

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…The algorithms that are coming under the category of automatic methods are proposed by the authors (Sridhar et al, 2002). Split and merge and the rest of the algorithms are semi-automated category of algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…A framework is proposed (Sridhar et al, 2002) which combine an effective algorithm to detect the brighter calculi and a validation process for detecting the calculi. So the algorithm has two phases.…”

Section: Proposed Methodmentioning

confidence: 99%

“…The proposed automatic algorithm (Sridhar et al, 2002) is used along with the existing algorithms like active contour models, Pyramidal Seeded Region growing algorithm; Morphology based watershed segmentation, Merge and split and region growing algorithms for detecting bright spots. The object contour is encoded using the Freeman chain code (Umbaugh, 2010).…”

Section: First Phasementioning

confidence: 99%

“…An algorithm is proposed by the authors (Sridhar et al, 2002) for the detection of renal calculi. This proposed segmentation framework validation criterion (Sridhar et al, 2002) is based on the presence of acoustic shadow caused by the calculi in ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

“…This proposed segmentation framework validation criterion (Sridhar et al, 2002) is based on the presence of acoustic shadow caused by the calculi in ultrasound images. The strength of the shadow depends on the physical characteristics of renal calculi.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of Ureteric and Bladder Calculi in Ultrasound Images

Sridhar¹

2012

Journal of Computer Science

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Problem statement:The framework for segmenting calculi in ureter and bladder using ultrasound images is proposed in this study. Calculi are due to abnormal collection of certain chemicals like oxalate, phosphate and uric acid. These calculi can be present in kidney, urethra or in urinary bladder. The extraction of calculi in medical images is a difficult task as no standard algorithms are available. This poses a serious a problem for successful diagnosis of calculi disease. The proposed technique is specific for the extraction of calculi in ureter and bladder. This constitutes the first stage in the successful treatment of calculi disease. Approach: An algorithm is proposed to detect calculus based on the shadow it casts in ultrasound image. Calculi are present in ultrasound images as bright spots. But noise in the image also can be bright spots. So it is easy to misinterpret the presence of noise as calculi. The proposed framework thus has two phases. In phase one, five standard algorithms are modified and are used to segment the bright spots present in the ultrasound images using the intensity profile. Calculi are having intensity in the range of 10-40. So all the potential calculi as well as the noise that appear as bright spots are segmented in phase one. In phase two,a validation procedure is used to validate the presence of calculi using its acoustic shadow property in the ultrasound images. Results: Ultrasound images of twenty-seven ureteric and bladder calculi patients are used for testing the framework. The detected calculi by the proposed framework are validated against a group of experts. The Performance of the proposed method is thoroughly investigated and the accuracy of the framework is determined. The framework incorporating automated seed selected region growing algorithm is able to detect the calculi with the efficiency of 78.57%. Conclusion: The extracted calculi can further be analyzed for taking decision about the treatment procedures. The proposed system is helpful for taking decision about the treatment procedures. The proposed system is helpful for clinicians as a decision support tool. This system can also be useful as educational aid for assisting or decision making in the treatment of calculus disease.

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Section: Discussionmentioning

confidence: 99%

Section: Proposed Methodmentioning

confidence: 99%

Section: First Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation of Ureteric and Bladder Calculi in Ultrasound Images

Sridhar¹

2012

Journal of Computer Science

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An algorithm for calculi segmentation on ureteroscopic images

Rosa¹,

Mozer

Szewczyk³

2010

Int J CARS

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Computer Aided Diagnosis System for Stone Detection and Early Detection of Kidney Stones

Tamilselvi¹,

Thangaraj²

2011

Journal of Computer Science

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Problem statement: Most of the previous study in diagnosis of kidney stone identifies a mere presence or absence of the stones in the kidney. However proposal in our study even present an early detection of kidney stones which helps to change the diet conditions and prevent the formation of stones. Approach: The study presented a scheme for ultrasound kidney image diagnosis for stone and its early detection based on improved seeded region growing based segmentation and classification of kidney images with stone sizes. With segmented portions of the images the intensity threshold variation helps in identifying multiple classes to classify the images as normal, stone and early stone stages. The improved semiautomatic Seeded Region Growing (SRG) based image segmentation process homogeneous region depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are extracted. The shape and size of the growing regions depend on this look up table entries. The region merging after the region growing also suppresses the high frequency artifacts. The diagnosis process is done based on the intensity threshold variation obtained from the segmented portions of the image and size of the portions compared to that of the standard stone sizes (less than 2 mm absence of stone, 2-4 mm early stages and 5mm and above presence of kidney stones). Results: The parameters of texture values, intensity threshold variation and stones sizes are evaluated with experimentation of various Ultrasound kidney image samples taken from the clinical laboratory. The texture extracted from the segmented portion of the kidney images presented in our study precisely estimate the size of the stones and the position of the stones in the kidney which was not done in the earlier studies. Conclusion: The integrated improved SRG and classification mechanisms presented in this study diagnosis the kidney stones presence and absence along with the early stages of stone formation

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Segmentation of renal calculi in ultrasound images

Cited by 6 publications

References 4 publications

Segmentation of Ureteric and Bladder Calculi in Ultrasound Images

Segmentation of Ureteric and Bladder Calculi in Ultrasound Images

An algorithm for calculi segmentation on ureteroscopic images

Computer Aided Diagnosis System for Stone Detection and Early Detection of Kidney Stones

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