Research Article Open Access

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

Ms. P.R. Tamilselvi and Dr. P. Thangaraj


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.

Journal of Computer Science
Volume 7 No. 2, 2011, 250-254


Submitted On: 2 December 2010 Published On: 25 February 2011

How to Cite: Tamilselvi, M. P. & Thangaraj, D. P. (2011). Computer Aided Diagnosis System for Stone Detection and Early Detection of Kidney Stones. Journal of Computer Science, 7(2), 250-254.

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  • Ultrasound images
  • kidney stones
  • early detection
  • image segmentation
  • Seeded Region Growing (SRG)
  • stone detection
  • Intensity threshold