Title :
An improved method of image segmentation using fuzzy c-means
Author :
Thamaraichelvi, B. ; Yamuna, G. ; Vanitha, U.
Author_Institution :
Dept. of Electr. Eng., Annamalai Univ., Annamalai Nagar, India
Abstract :
We propose a new method by incorporating improved k-means and modified fuzzy c-means clustering techniques for segmenting medical images. Segmentation of medical images plays a key role in estimating the object boundary and abnormalities, if any. Moreover, it is an important process to extract more information from complex medical images. Magnetic resonance images may contain noise due to the operator performance and the interference of radio frequency coil; hence it makes a challenging problem for segmenting such images in medical field. Fuzzy C-Means (FCM) is a flexible approach for the automatic segmentation of medical images. K-means has low computational complexity.
Keywords :
biomedical MRI; feature extraction; image segmentation; learning (artificial intelligence); medical image processing; pattern clustering; computational complexity; fuzzy c-means clustering technique; improved k-means clustering technique; magnetic resonance image; medical image extraction; medical image segmentation; object abnormality; object boundary; Biomedical imaging; Humans; Image edge detection; Image segmentation; Magnetic resonance imaging; Brain MR imaging; Fuzzy c-means clustering; K-means; bias field; center initialization; image segmentation;
Conference_Titel :
Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on
Conference_Location :
Nagapattinam, Tamil Nadu
Print_ISBN :
978-1-4673-0213-5
