Automatic region of interest generation for kidney ultrasound images
Ultrasound scanning of the kidney is performed to assess kidney size, shape and location as well as to detect any abnormalities in kidney like cysts and stones. Since ultrasound image contains speckle noise, performing the segmentation methods for the kidney images has always been a very challenging...
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| Main Authors: | , |
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| 格式: | Conference or Workshop Item |
| 出版: |
2011
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| 主题: | |
| 在线阅读: | http://eprints.uthm.edu.my/5708/ http://eprints.uthm.edu.my/5708/1/penang_proc.pdf |
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| 总结: | Ultrasound scanning of the kidney is performed to assess kidney size, shape and location as well as
to detect any abnormalities in kidney like cysts and stones. Since ultrasound image contains speckle noise,
performing the segmentation methods for the kidney images has always been a very challenging task. For
further segmentation purpose, deleting and removing the complicated background not only speeds up the
segmentation process, but also increases accuracy. However, in previous studies, the ROI of the kidney is
manually cropped. Therefore, this study proposed an automatic region of interest (ROI) generation for kidney
ultrasound images. The methods consist of the speckle noise reduction using Gaussian low-pass filter, texture
analysis by calculating the local entropy of the image, threshold selection, morphological operations, object
windowing, determination of seed point and last but not least the ROI generation. This algorithm has been
tested to more than 200 kidney ultrasound images. As the result, for longitudinal kidney images, out of 120
images, 109 images generate true ROI (91%) and another 11 images generate false ROI (9%). For transverse
kidney images, out of 100 images, 89 images generate true ROI (89%) and 11 images generate false ROI
(11%). To conclude, the method in this study can be practically used for automatic generation of US kidney
ROI. |
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