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MPD12 RETINAL EXUDATES SEGMENTATION USING K MEANS CLUSTERING. ABSTRACT In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard), however there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data, however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step; therefore, they do not require labeled lesion training sets which are time consuming to create, difficult to obtain and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time. TOOLS USED: MATLAB 7.8 R2009a IMAGE PROCESSING TOOL BOX www.wineyard.in MPD12 THE MATLAB LANGUAGE: This is a high-level matrix/ array language with control flow statements, functions, data structures, input/output, and object-oriented programming features. It allows both programming in the small “to rapidly create quickly and dirty throw away programs”, and programming in the large “to create complete large and complex application programs”. www.wineyard.in