Segmentation of Ureteric and Bladder Calculi in Ultrasound Images | Science Publications

Journal of Computer Science

S. Sridhar¹ 

关键词: Kidney stones;    ureteric calculi;    bladder calculi;    automatic segmentation;    ultrasound images;    decision support systems;   

DOI  :  10.3844/jcssp.2012.716.720

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

Unknown