Optic Nerve Head Info Injected K-Means Method for Segmentation of OD

Segmenting the Optic Disc (OD) is an important step in the automated detecting of certain serious ocular illnesses. An innovative strategy for segmenting the OD is critical since it faces challenges such as vascular obstruction, low contrast, and poor accuracy. This paper proposes a new method for OD segmentation, namely 'OD segmentation using Optic Nerve Head info injected K-Means method (ONHKM)'. By adding Optic Nerve Head (ONH) structural information through an iterative procedure, the proposed method improves upon the standard K-means segmentation algorithm. Through this integration, the membership matrix is updated gradually based on the ONH structure information that improves the segmentation and identification of the OD region. The contributions through the exclusive membership update and objective function update, make up the ONH structure-specific OD enhancement. The ONH structure-specific membership modification enhances the efficiency of OD segmentation, while the modified OD structure-specific objective function leverages the execution speed of the convergence process. The proposed ONHKM method exhibited an impressive average segmentation accuracy of 97.52% compared to existing methods. The efficacy of the method is shown by this major enhancement, which can be effortlessly incorporated into fundus scanners, offering a useful and dependable tool for medical use. According to the results, the ONHKM method improves automated OD segmentation, which will hopefully assist with ocular disease early diagnosis and treatment.