Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms.
This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FCM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFCM), was used for MRI segmentation in Ophthalmology. These unsupervised segmentation algorithms can help Ophthalmologists to reduce the medical imaging noise effects originating from low resolution sensors and/or the structures that move during the data acquisition. They may be particularly helpful in the clinical oncological field as an aid to the diagnosis of Retinoblastoma, an inborn oncological disease in which symptoms usually show in early childhood. For the purpose of early treatment with radiotherapy and surgery, the newly proposed AFCM is preferred to provide more information for medical images used by Ophthalmologists. Comparisons between FCM and AFCM segmentations are made. Both fuzzy clustering segmentation techniques provide useful information and good results. However, the AFCM method has better detection of abnormal tissues than FCM according to a window selection. Overall, the newly proposed AFCM segmentation technique is recommended in MRI segmentation.
Yang
Miin Shen
MS
Department of Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan 32023. msyang@math.cycu.edu.tw
Hu
Yu Jen
YJ
Lin
Karen Chia-Ren
KC
Lin
Charles Chia-Lee
CC
D000465:Algorithms; D002675:Child, Preschool; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008279:Magnetic Resonance Imaging; D019572:Retinal Neoplasms / Q000175:diagnosis*; D012175:Retinoblastoma / Q000175:diagnosis*