Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

Abstract Diabetic Retinopathy (DR) is one of the earliest signs of a complication of diabetes mellitus and is also a leading cause of visual blindness globally. Recent studies have proposed that exudates (EXs) are the hallmark of DR severity. The present study aims to accurately and automatically detect EXs related features that are difficult to detect in fundus photography in early stages. The proposed method utilizes a Fusion of Histogram-Based Fuzzy C-Means clustering (FHBFCM) based on a New Weight Assignment Scheme (NWAS), and a set of four selected features from stages of pre-processing in order to evolve the detection method. Through the coarse segmentation stage, the optimal parameter of FHBFCM is trained by the features of DR for detection of EXs diseases through a stepwise enhancement process. The histogram-based is used to find the color intensity in each of the pixels and performed to accomplish RGB color information. This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by FCM method. Afterwards, the best expression of NWAS is then used for fine segmentation stage. The experimental results show that the proposed method achieves accuracy values of 96.12%, 97.20%, 93.22% for EXs segmentation on DiaretDB0, DiaretDB1, and STARE fundus image datasets respectively. In conclusion, this study provides a new method for early detection of EXs related features with a competitive accuracy and its ability to outperforms the other stateof-the-art by optimizing the detection quality, and possibly reducing the false positive segmentation in a significant way.