Abstract
Image Segmentation is the process of dividing an image into regions or objects for the purpose of extracting useful information. It plays vital and dominant role in medical image analysis. OCT is a high speed and non-invasive method to determine three dimensional images of retina. This paper presents an improved method of automated OCT image segmentation in order to detect and classify Retinopathy diseases. Discrete wavelet transform (DWT) is a multiresolution approach and is widely used for OCT image segmentation. K-Means Cluster (KMC) approach is popular among researchers that offers better results for extracting the features for image segmentation. This work compares the segmentation process based on DWT with KMC and presents a better segmentation method comprising of K-Means Cluster with Genetic Algorithm Optimization (KMC-GAO) that identifies cluster centroid for obtaining the improved image segmentation performance of an OCT image. The performance metrics such as Structural Content (SC), Rand Index (RI), Variation of Information (VOI) and Global Consistency Error (GCE) are evaluated for all these segmentation techniques and it is observed that KMC-GAO segmentation offers better result than DWT method and KMC approach.
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 callMore From: International Journal of Recent Technology and Engineering (IJRTE)
Disclaimer: 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.
