A Novel Retinal Vessel Segmentation Based On Histogram Transformation Using 2-D Morlet Wavelet and Supervised Classification
This is an incremental improvement for medical image analysis, specifically aiding in disease diagnosis through automated retinal vessel segmentation.
The paper tackles retinal vessel segmentation by proposing a method using local histogram equalization, Morlet wavelet features, and Bayesian classification with GMM, achieving an accuracy of 0.9571 on the DRIVE database.
The appearance and structure of blood vessels in retinal images have an important role in diagnosis of diseases. This paper proposes a method for automatic retinal vessel segmentation. In this work, a novel preprocessing based on local histogram equalization is used to enhance the original image then pixels are classified as vessel and non-vessel using a classifier. For this classification, special feature vectors are organized based on responses to Morlet wavelet. Morlet wavelet is a continues transform which has the ability to filter existing noises after preprocessing. Bayesian classifier is used and Gaussian mixture model (GMM) is its likelihood function. The probability distributions are approximated according to training set of manual that has been segmented by a specialist. After this, morphological transforms are used in different directions to make the existing discontinuities uniform on the DRIVE database, it achieves the accuracy about 0.9571 which shows that it is an accurate method among the available ones for retinal vessel segmentation.