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A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

P. R. Tamilselvi, P. Thangaraj
Copyright: © 2012 |Volume: 8 |Issue: 1 |Pages: 16
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781466612990|DOI: 10.4018/jiit.2012010104
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MLA

Tamilselvi, P. R., and P. Thangaraj. "A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images." IJIIT vol.8, no.1 2012: pp.46-61. http://doi.org/10.4018/jiit.2012010104

APA

Tamilselvi, P. R. & Thangaraj, P. (2012). A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images. International Journal of Intelligent Information Technologies (IJIIT), 8(1), 46-61. http://doi.org/10.4018/jiit.2012010104

Chicago

Tamilselvi, P. R., and P. Thangaraj. "A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images," International Journal of Intelligent Information Technologies (IJIIT) 8, no.1: 46-61. http://doi.org/10.4018/jiit.2012010104

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Abstract

Segmentation of stones from abdominal ultrasound images is a unique challenge to the researchers because these images have heavy speckle noise and attenuated artifacts. In the previous renal calculi segmentation method, the stones were segmented from the medical ultra sound kidney stone images using Adaptive Neuro Fuzzy Inference System (ANFIS). But, the method lacks in sensitivity and specificity measures. The segmentation method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new segmentation method is proposed in this paper. Here, new region indicators and new modified watershed transformation is utilized. The proposed method is comprised of four major processes, namely, preprocessing, determination of outer and inner region indictors, modified watershed segmentation with ANFIS performance. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of proposed segmentation method in segmenting the kidney stones and the achieved improvement in sensitivity and specificity measures. Furthermore, the performance of the proposed technique is evaluated by comparing with the other segmentation methods.

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