Improved detection and analysis of Macular Edema using modified guided image filtering with modified level set spatial fuzzy clustering on Optical Coherence Tomography images

Abstract

Background and Objective: The hypo-reflective Optical Coherence Tomography (OCT) imaging of the pseudo retinal layer strengthens the analysis of various retinal disorders, including Macular Edema (ME)/Diabetic Macular Edema (DME). The primary challenge in the automatic identification and analysis of ME cases is the presence of multiplicative speckle noise and variational edge boundaries. This paper presents an automated algorithm for detecting ME and cyst segmentation. Methods: The algorithm is implemented in three steps. The first step includes the removal of OCT’s predominating speckle noise. In this regard, edge-preserving modified guided image filtering (mGIF) has been employed. After that, the boundaries of retinal layers are segmented using a modified level set spatial fuzzy clustering (LSFCM) algorithm. This process identifies the inner limiting membrane and retinal pigment epithelium layer. The macular thickness between these layers is essential for the detection of edema cases. The third step identifies the presence of cystoid fluid inside positive edema cases using a modified Nick’s threshold, followed by modified LSFCM, applied to mGIF filtered green channel OCT images. Results: The algorithm is tested on a local database (DS1) containing 260 OCT (187 normal and 73 diseased) images. The algorithm’s performance is validated using standard database verification protocols considering the Duke University DME database (DS2) and OPTIMA database (DS3). The databases are annotated by two professional ophthalmologists. The method records an average 97.37% accuracy and an F1-score of 98.69% in detecting ME cases in the DS1 and DS2 databases. The average sensitivity, specificity, accuracy, precision, recall, and F1-score obtained for cyst detection are 98.92%, 99.22%, 98.95%, 98.33%, 98.92% and 98.25%, respectively, obtained from DS1, DS2 database positive ME cases, and DS3 database. The algorithm enhances OCT analysis with comparable efficiency over current state-of-the-art methodologies. Conclusion: The algorithm enhances OCT analysis with efficient speckle noise removal and variational boundary detection of the OCT layer with comparable efficiency over current state-of-the-art methodologies.