Robust watershed segmentation using wavelets
Image and Vision Computing
A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering
IEEE Transactions on Image Processing
Combining Supervised Learning Techniques to Key-Phrase Extraction for Biomedical Full-Text
International Journal of Intelligent Information Technologies
An Intelligent Operator for Genetic Fuzzy Rule Based System
International Journal of Intelligent Information Technologies
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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.