Landmark Detection in Retinal Images
Gaurav Mittal (Home Page)
Advances in medical field and imaging systems have resulted in a series of devices that sense, record, transform and process digital data. In the case of human eyes this digital data is fundus images, which are images of the back part of our retina. Automatic analysis of these images is required to process large amount of data and help doctors make the final diagnosis. Retina images has 3 major visible landmarks: Optic disk(OD), macula and blood vessels. In retina images, OD appears as a bright elliptical structure, macula appear as a small dark region and blood vessel appears as dark tree branch like structure. In this thesis, we have proposed methods for detection of retina landmarks. Accurate detection of OD and macula is important as computer assisted diagnosis systems uses location of these landmarks for understanding the retina image and using clinical facts about retina for improving diagnosis. Retina landmark detection also aids in assessing the severity of diseases based on the locations of abnormalities relative to these landmarks. We first used retina atlas for OD and macula detection. The idea of retina atlas is inspired by brain 3D atlas . We create 2 retina atlases: intensity atlas and probability atlas, by annotating public datasets locally. We use probabilistic atlas for OD and macula detection but detection rates and accuracy of the system is low. To achieve better detection, we than used Generalized motion patterns(GMP)  for OD and macula detection. The GMP is derived by inducing motion to an image, which serves to smooth out unwanted information while highlighting the structures of interest. Our GMP based detection is fully unsupervised and its results outperformed all other unsupervised methods. The results are comparable to that of supervised methods. The proposed GMP based system is completely parallelizable and handles illumination differences efficiently. Blood vessels are another important retina landmark and we find that the current research uses evaluation measure like sensitivity, specifity, accuracy, area under curve and matthew correlation coefficient for evaluating vessel segmentation performance. We find several gaps in current evaluation measures and propose local accuracy, which is an extension of . We show that local accuracy is especially useful in settings, where segmentation of weak vessels and accurate estimation of vessel width is required.
|Year of completion:
Gaurav Mittal, Jayanthi Sivaswamy - Optic Disk and Macula Detection from Retinal Images using Generalized Motion Pattern Proceedings of the Fifth National Conference on Computer Vision Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2015), 16-19 Dec 2015, Patna, India. [PDF]