Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
º¹ºÎ CT ¿µ»ó¿¡¼ ´ÙÁß ¾ÆƲ¶ó½º ±â¹Ý Çü»ó ¹× ¹à±â°ª Á¤º¸¸¦ »ç¿ëÇÑ ½Å½ÇÁú ÀÚµ¿ ºÐÇÒ |
¿µ¹®Á¦¸ñ(English Title) |
Automatic Segmentation of Renal Parenchyma using Shape and Intensity Information based on Multi-atlas in Abdominal CT Images |
ÀúÀÚ(Author) |
±èÇöÁø
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Hyeonjin Kim
Helen Hong
Kidon Chang
Koon Ho Rha
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¿ø¹®¼ö·Ïó(Citation) |
VOL 45 NO. 09 PP. 0937 ~ 0942 (2018. 09) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
Renal parenchyma segmentation is necessary to predict contralateral hypertrophy after renal partial nephrectomy. In this paper, we propose an automatic segmentation method of renal parenchyma using shape and intensity information based on the multi-atlas in abdominal CT images. First, similar atlases are selected using volume-based similarity registration and intensity-similarity measure. Second, renal parenchyma is segmented using two-stage registration and constrained intensity-based locally-weighted voting. Finally, renal parenchyma is refined using a Gaussian mixture model-based multi-thresholds and shape-prediction map in under- and over-segmented data. The average dice similarity coefficient of renal parenchyma was 91.34%, which was 18.19%, 1.35% higher than the segmentation method using majority voting and locally-weighted voting in dice similarity coefficient, respectively.
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Å°¿öµå(Keyword) |
ÄÄÇ»ÅÍ ´ÜÃþÃÔ¿µ ¿µ»ó(CT)
½Å½ÇÁú
´ÙÁß ¾ÆƲ¶ó½º
Áö¿ªÀû °¡ÁßÅõÇ¥(LWV)
Çü»óÈ®·ü¸Ê(SPM)
computed tomography (CT)
renal parenchyma
multi-atlas segmentation
locally weighted voting
shape-prediction map
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ÆÄÀÏ÷ºÎ |
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