Segmentation of renal calculi in ultrasound images

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

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.

An algorithm for calculi segmentation on ureteroscopic images

PURPOSE

The purpose of the study is to develop an algorithm for the segmentation of renal calculi on ureteroscopic images. In fact, renal calculi are common source of urological obstruction, and laser lithotripsy during ureteroscopy is a possible therapy. A laser-based system to sweep the calculus surface and vaporize it was developed to automate a very tedious manual task. The distal tip of the ureteroscope is directed using image guidance, and this operation is not possible without an efficient segmentation of renal calculi on the ureteroscopic images.

METHODS

We proposed and developed a region growing algorithm to segment renal calculi on ureteroscopic images. Using real video images to compute ground truth and compare our segmentation with a reference segmentation, we computed statistics on different image metrics, such as Precision, Recall, and Yasnoff Measure, for comparison with ground truth.

RESULTS

The algorithm and its parameters were established for the most likely clinical scenarii. The segmentation results are encouraging: the developed algorithm was able to correctly detect more than 90% of the surface of the calculi, according to an expert observer.

CONCLUSION

Implementation of an algorithm for the segmentation of calculi on ureteroscopic images is feasible. The next step is the integration of our algorithm in the command scheme of a motorized system to build a complete operating prototype.