In Our Project, Feature vectors are composed of the pixel’s intensity and continuous two-dimensional stationary wavelet transform responses taken at multiple scales. The Stationary wavelet is capable of turning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a neural network on training with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. To implement an effective algorithm based on Morphological process and Segmentation Techniques to detect the Retina vessels and Exudates from an eye fundus image. The combination of multi-structure morphological process and Segmentation technique is used effectively for retinal vessel and exudates detection here. The modules made here are 1. Retina Blood Vessels Detection in which Plane separation, Contrast Enhancement, Morphological processes are done under this module. 2. Exudates Detection in which Segmentation Technique is used.
- The segmentation step requires prior knowledge of discriminated image features
- Principal Component Analysis
Drawbacks of Exisitng System
- High Computational load and poor discriminatory power.
- SVM is slow training for large feature sets.
- Less accuracy in classification
- GLCM Features
- NN Classifier
- K-means Clustering
- The segmentation algorithm Proves to be simple and effective
- The greyscale Co-occurrence matrix performed well in NN
- Better texture and edge representation
- Segmentation provides better clustering efficiency
- 4 GB of RAM
- 500 GB of Hard disk
- MATLAB 2014a
 Paweł Liskowski, Krzysztof Krawiec, Member, Citation information: DOI 10.1109/TMI.2016.2546227, IEEE Transactions on Medical Imaging “Segmenting Retinal Blood Vessels with Deep Neural Networks”.
 R. Nekovei and Y. Sun, “Back-propagation network and its configuration for blood vessel detection in angiograms.” IEEE Transactions on Neural Networks, vol. 6, no. 1, pp. 64–72, 1995. [Online]. Available: http://dblp.uni trier.de/db/journals/ tnn/tnn6.html #NekoveiS95
 G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580, 2012.
 Y. Bengio, “Learning deep architectures for ai,” Foundations and trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.
 J. Schmidhuber, “Deep learning in neural networks: An overview,”
Neural Networks, vol. 61, pp. 85–117, 2015.