DocumentCode :
333375
Title :
Segmentation of ultrasonic ovarian images by texture features
Author :
Jiang, Ching-Fen ; Chen, Mu-Long
Author_Institution :
Dept. of Electron. Eng., I-Shou Univ., Kaohsiung, Taiwan
Volume :
2
fYear :
1998
fDate :
29 Oct-1 Nov 1998
Firstpage :
850
Abstract :
Auto-segmenting two-dimensional images of the ovary into non-ovarian, normal ovarian, and abnormal ovarian regions is required when using ultrasonic image to detect ovarian cancer. The texture-based segmentation method presented here is a pixel classifier based on four texture energy measures associated with each pixel in the images. The 25 two-dimensional feature masks are derived from 3 basic one-dimensional vectors to evaluate the classification results. Four of those features are selected as the bases for the automated clustering procedure. The segmented images produced as the result of applying the algorithm to an example image are presented and discussed. The automated clustering algorithm with these texture-feature masks has been found to hold promise as an automated segmentation method for ultrasonic ovarian images
Keywords :
biological organs; biomedical ultrasonics; cancer; gynaecology; image segmentation; image texture; medical image processing; vectors; automated clustering algorithm; basic one-dimensional vectors; medical diagnostic imaging; most mortal female cancer; ovarian cancer; pixel classifier; texture features; two-dimensional feature masks; ultrasonic ovarian images; ultrasonic ovarian images segmentation; Cancer detection; Clustering algorithms; Convolution; Detectors; Energy measurement; Image edge detection; Image segmentation; Pixel; Power engineering and energy; Ultrasonography;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE
Conference_Location :
Hong Kong
ISSN :
1094-687X
Print_ISBN :
0-7803-5164-9
Type :
conf
DOI :
10.1109/IEMBS.1998.745570
Filename :
745570
Link To Document :
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