Download MPD12 RETINAL EXUDATES SEGMENTATION USING K MEANS

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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
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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”.
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