Automatic generation of region of interest for kidney ultrasound images using texture analysis
Kidney ultrasound imaging can be used to estimate kidney size and position, and help to diagnose structural abnormalities as well as the presence of cysts and stones. However, due to the presence of speckle noise in ultrasound images, performing the segmentation methods for the kidney images were v...
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| Format: | Article |
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Research Bible
2012
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| _version_ | 1796856915533234176 |
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| author | Wan M., Hafizah Supriyanto, Eko |
| author_facet | Wan M., Hafizah Supriyanto, Eko |
| author_sort | Wan M., Hafizah |
| collection | ePrints |
| description | Kidney ultrasound imaging can be used to estimate kidney size and position, and help to diagnose structural abnormalities as well as the presence of cysts and stones. However, due to the presence of speckle noise in ultrasound images, performing the segmentation methods for the kidney images were very challenging and therefore, deleting and removing the complicated background will speeds up and increases the accuracy of the segmentation process. However, in previous studies, the ROI of the kidney is manually cropped. Therefore, this study proposed an automatic region of interest (R OI) generation for kidney ultrasound images. Firstly, some techniques of speckle noise reduction were implemented consist of median filter, Wiener filter and Gaussian low-pass filter. Then texture analysis was performed by calculating the local entropy of the image, continued with the threshold selection, morphological ope rations, object windowing, determination of seed point and last but not least the ROI generation. This method was performed to several kidney ultrasound images with different speckle noise reduction techniques and different threshold value selection. Based on the result, it shows that for median filter, threshold value of 0.6 gave the highest TRUE ROIs which were 70%. For Wiener filter, threshold value of 0.8 gave highest TRUE ROIs which were 80% and for Gaussian lo w-pass filter, threshold value of 0.7 gave highest TRUE ROIs which were 100%. By using the previous result, this method has b een tested also to more than 200 kidney ultrasound images. As the result, for longitudinal kidney images, out of 120 imag es, 109 images generate true ROI (91%) and another 11 images generate false ROI (9%). For transverse kidney images, out of 100 imag es, 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. |
| first_indexed | 2024-03-05T18:49:58Z |
| format | Article |
| id | utm.eprints-31787 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:49:58Z |
| publishDate | 2012 |
| publisher | Research Bible |
| record_format | dspace |
| spelling | utm.eprints-317872019-01-28T03:50:03Z http://eprints.utm.my/31787/ Automatic generation of region of interest for kidney ultrasound images using texture analysis Wan M., Hafizah Supriyanto, Eko RA0421 Public health. Hygiene. Preventive Medicine Kidney ultrasound imaging can be used to estimate kidney size and position, and help to diagnose structural abnormalities as well as the presence of cysts and stones. However, due to the presence of speckle noise in ultrasound images, performing the segmentation methods for the kidney images were very challenging and therefore, deleting and removing the complicated background will speeds up and increases the accuracy of the segmentation process. However, in previous studies, the ROI of the kidney is manually cropped. Therefore, this study proposed an automatic region of interest (R OI) generation for kidney ultrasound images. Firstly, some techniques of speckle noise reduction were implemented consist of median filter, Wiener filter and Gaussian low-pass filter. Then texture analysis was performed by calculating the local entropy of the image, continued with the threshold selection, morphological ope rations, object windowing, determination of seed point and last but not least the ROI generation. This method was performed to several kidney ultrasound images with different speckle noise reduction techniques and different threshold value selection. Based on the result, it shows that for median filter, threshold value of 0.6 gave the highest TRUE ROIs which were 70%. For Wiener filter, threshold value of 0.8 gave highest TRUE ROIs which were 80% and for Gaussian lo w-pass filter, threshold value of 0.7 gave highest TRUE ROIs which were 100%. By using the previous result, this method has b een tested also to more than 200 kidney ultrasound images. As the result, for longitudinal kidney images, out of 120 imag es, 109 images generate true ROI (91%) and another 11 images generate false ROI (9%). For transverse kidney images, out of 100 imag es, 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. Research Bible 2012 Article PeerReviewed Wan M., Hafizah and Supriyanto, Eko (2012) Automatic generation of region of interest for kidney ultrasound images using texture analysis. International Journal of Biology and Biomedical Engineering, 6 (1). pp. 26-34. ISSN 1998-4510 http://www.naun.org/multimedia/NAUN/bio/17-520.pdf |
| spellingShingle | RA0421 Public health. Hygiene. Preventive Medicine Wan M., Hafizah Supriyanto, Eko Automatic generation of region of interest for kidney ultrasound images using texture analysis |
| title | Automatic generation of region of interest for kidney ultrasound images using texture analysis |
| title_full | Automatic generation of region of interest for kidney ultrasound images using texture analysis |
| title_fullStr | Automatic generation of region of interest for kidney ultrasound images using texture analysis |
| title_full_unstemmed | Automatic generation of region of interest for kidney ultrasound images using texture analysis |
| title_short | Automatic generation of region of interest for kidney ultrasound images using texture analysis |
| title_sort | automatic generation of region of interest for kidney ultrasound images using texture analysis |
| topic | RA0421 Public health. Hygiene. Preventive Medicine |
| work_keys_str_mv | AT wanmhafizah automaticgenerationofregionofinterestforkidneyultrasoundimagesusingtextureanalysis AT supriyantoeko automaticgenerationofregionofinterestforkidneyultrasoundimagesusingtextureanalysis |