Publisher： Edp Sciences
E-ISSN： 2261-236x|61|issue|02010-02010
ISSN： 2261-236x
Source： MATEC Web of conference, Vol.61, Iss.issue, 2016-06, pp. : 02010-02010
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
Here we analyze the difficulties of segmentation without tag line of left ventricle MR images, and propose an algorithm for automatic segmentation of left ventricle (LV) internal and external profiles. Herein, we propose an Incomplete K-means and Category Optimization (IKCO) method. Initially, using Hough transformation to automatically locate initial contour of the LV, the algorithm uses a simple approach to complete data subsampling and initial center determination. Next, according to the clustering rules, the proposed algorithm finishes MR image segmentation. Finally, the algorithm uses a category optimization method to improve segmentation results. Experiments show that the algorithm provides good segmentation results.
Related content
Residential power user segmentation based on k-means clustering method in the context of big data
E3S Web of conferences, Vol. 53, Iss. issue, 2018-09 ,pp. :
Access to resources
Recommend
An Interactive Segmentation Method Based on Superpixel
MATEC Web of conference, Vol. 22, Iss. issue, 2015-07 ,pp. :
MQAM Modulation Recognition Based on AP Clustering Method
MATEC Web of conference, Vol. 44, Iss. issue, 2016-02 ,pp. :