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Liver Ultrasound Image Segmentation Using Region-Difference Filters
- Publication Year :
- 2016
- Publisher :
- Springer International Publishing, 2016.
-
Abstract
- In this paper, region-difference filters for the segmentation of liver ultrasound (US) images are proposed. Region-difference filters evaluate maximum difference of the average of two regions of the window around the center pixel. Implementing the filters on the whole image gives region-difference image. This image is then converted into binary image and morphologically operated for segmenting the desired lesion from the ultrasound image. The proposed method is compared with the maximum a posteriori-Markov random field (MAP-MRF), Chan-Vese active contour method (CV-ACM), and active contour region-scalable fitting energy (RSFE) methods. MATLAB code available online for the RSFE method is used for comparison whereas MAP-MRF and CV-ACM methods are coded in MATLAB by authors. Since no comparison is available on common database for the performance of the three methods, therefore, performance comparison of the three methods and proposed method was done on liver US images obtained from PGIMER, Chandigarh, India and from online resource. A radiologist blindly analyzed segmentation results of the 4 methods implemented on 56 images and had selected the segmentation result obtained from the proposed method as best for 46 test US images. For the remaining 10 US images, the proposed method performance was very near to the other three segmentation methods. The proposed segmentation method obtained the overall accuracy of 99.32% in comparison to the overall accuracy of 85.9, 98.71, and 68.21% obtained by MAP-MRF, CV-ACM, and RSFE methods, respectively. Computational time taken by the proposed method is 5.05 s compared to the time of 26.44, 24.82, and 28.36 s taken by MAP-MRF, CV-ACM, and RSFE methods, respectively.
- Subjects :
- Databases, Factual
Computer science
Scale-space segmentation
India
Image processing
02 engineering and technology
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Image texture
0202 electrical engineering, electronic engineering, information engineering
Humans
Radiology, Nuclear Medicine and imaging
Computer vision
Segmentation
Ultrasonography
Active contour model
Radiological and Ultrasound Technology
Pixel
business.industry
Binary image
Pattern recognition
Image segmentation
Computer Science Applications
Liver
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithms
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....088cd0cfed547e5bd7324a30caaec51a