Abstract
Liver segmentation in CT images is still a challenge in both radiology, medical image processing and machine learning. Due to the limitations from imaging procedure and other factors, most of liver CT images suffer from noise, edge blur and so on. In order to overcome the difficulties in liver CT image segmentation, a segmentation method based on region-scalable fitting model(RSF) is proposed. RSF model defines a data fitting energy and two fitting functions that locally approximate the image intensities on the two sides of the contour. After segmenting by RSF model, histogram statistics is employed for post-processing. Experimental results for liver CT images demonstrated desirable performances of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
