Retinal Image Analysis
Retinal images are widely used for dignostic purposes by opthalmologists. They provide vital information about the health of the sensory part of the visual system.
Several diseases that can lead to blindness manifest as artifacts in the retinal image. Automatic segmentation and analysis of retinal images can therefore be used to diagnose these diseases.
Our current work focuses on following areas:
- General Segmentation
- Uni-modal and cross-modal registration
- Disease Analysis
- Content Based Image Retrieval (CBIR) of Retinal Images
General Segmentation - Developing techniques for segmenting various structures of interest such as blood vessel tree, optic disk and the macula within the retina.
Uni-modal and cross-modal registration Developing techniques for retinal image registration in order to combine the complementary information in different and same retinal image modalities image.
Disease Analysis - Developing techniques for identifying, quantifying and tracking signs of different types of diseases.
Some of the projects in disease analysis are :
- Detection and quantification of lesions that occur at very early stages of Diabetic Retinopathy (DR). Examples of such lesions are microaneurysms and hard exudates. The aim is to detect these from color fundus images as it is of prime importance in developing solutions for screening programmes among large populations.
- Detection of Capillary non-perfusion (CNP), which occurs in advanced stages of DR. The aim is to detect and quantify the total area covered by these lesions from FFA images.
- Detection, counting and grading of drusen, that occur due to Age-related macular degeneration(AMD)
Content Based Image Retrieval (CBIR) of Retinal Images
Image search through Content Based Image Retrieval (CBIR) is a challenging problem in a large database. This becomes more complex in medical images as the interest for retrieval is based on semantics (pathology/anatomy) rather than just the visual similarity.
We are currently working on devising a CBIR solution for retinal images in the ophthalmology department of hospitals. By applying CBIR in medical image databases we aim to assist ophthalmologists and students for teaching and self training on the computer. This is based on the assumption that visual characteristics of a disease carry diagnostic information and often visually similar images correspond to the same disease category.
- Arunava Chakravarty
Retinal Image Datasets
- Capillary Nonperfusion (CNP) Analysis Dataset *
- Age-related Macular Degeneration (AMD) Analysis Dataset *
- Optic Nerve Head (ONH) segmentation Dataset (Drishiti-GS 1)
* - available on request
Other Available Datasets
- Digital Retinal Images for Vessel Extraction (DRIVE)
- STructured Analysis of the Retina (STARE)
- Standard Diabetic Retinopathy Database (DIARETDB0 & DIARETDB1)
- Methods to evaluate segmentation and indexing techniques (MESSIDOR)
- Test suite of 18 challenging pairs of images for testing registration algorithms
- Image Database and Archive (RISA)
- Collection of multispectral images of the fundus
Other Retinal Image Analysis Groups
- Department of computer Science, University of Bristol
- Retinal Image Analysis Group: Columbia University
- Retinopathy image search and analysis (RISA)
- Structured Analysis of the Retina (STARE)
- DRIVE : Digital Retinal Images for Vessel Extraction
Retinal Image Tools
* Available Soon
- Retinopathy Online Challenge (ROC) is underway
- DIARETDB released
- Selected conferences in the field of Medical Imaging
- Medical Image Analysis Journals and Conference Proceedings
- Conferences Computer Vision, Image Processing and Medical Image Analysis
- Medical Imaging Computing
- European Society of Retina Specialists (EURETINA)
- Digital Healthcare Conference
- The International Diabetes Federation (IDF)