Population screening for sight threatening diseases based on fundus imaging is in place or being considered worldwide. Most existing programs are focussed on a specific disease and are based on manual reading of images, though automated image analysis based solutions are being developed. Exudates and drusen are bright lesions which indicate very different diseases, but can appear to be similar. Discriminating between them is of interest to increase screening performance. In this paper, we present a Bag of Words approach which can be used to design a system that can play the dual role of content based retrieval (of images with exudates or drusen) system and a decision support system to address the problem of bright lesion discrimination. The approach consists of a novel partitioning of an image into patches from which color, texture, edge and granulometry based features are extracted to build a dictionary. A bag of Words approach is then employed to help retrieve images matching a query image as well as derive a decision on the type of bright lesion in the given (query) image. This approach has been implemented and tested on a combination of public and local dataset of 415 images. The area under the curve for image classification is 0.90 and retrieved precision is 0.76. 1. INTRODUCTION Diabetic Retinopathy (DR) and Age Related Macular Degeneration (AMD) are among the major causes of irreversible blindness in the world. At the early stages, DR is characterized by red and bright lesions (called exudates) which appear as bright, reflective, white or cream colored lesions in color fundus images. AMD is chiefly characterized by drusen which appear as small yellow or white spots located throughout the fundus but more so in the macular region. Thus both drusen and exudates share much in appearance. This is a potential source of problem for readers or a computer aided diagnostic (CAD) tool based screening scenario designed for DR. CAD solutions are needed to help distinguish between the bright lesions. This can be done by designing a system which i) classifies a given bright lesion image as having exudates or drusen or ii) retrieves similar looking images from the collection of the screening program along with labels given by experts. A system which can do both is attractive as it allows flexibility in the type of end use that can be supported. We employ a Bag of Words (BoW) approach to the problem and offer a solution which results in both retrieval and classification of a given (query) image with bright lesions. [1, 2, 3] rely on accurate segmentation of the lesions for image level decision between drusen and exudates. Therefore, these methods can suffer due to lack of existing accurate lesion detection methods. Moreover, automatic detection of MA and dark lesions for contextual information in itself is a challenging task. In [4] a grid based approach is adopted to overcome this problem. However, processing of a large number of image patches increases computational load. Moreover, the grid size remains same across images which may be of different resolution and orientation. CBIR-based approaches have been explored for detecting and staging diabetic macular edema [5, 6, 7] , distinguishing AMD from Stargardts disease [8] as well as distinguishing among proliferative, non-proliferative DR and AMD [9] . All these approaches divide the image into regions and perform a local analysis to index a given image with the regions chosen to be anatomically meaningful [10, 9] . Analysis is based on features ranging from local binary patterns (LBP) [5] to AM-FM [9, 8] which are extracted with or without lesion segmentation. We present a CBIR-based approach for identifying and retrieving images with drusen or exudates. 3. METHODS The pipeline of the proposed system is shown in Figure 1 . The input image is divided into patches using the macula and optic disk (OD) as reference coordinates. Next a set of features are extracted from each image patch to help discriminate between normal, exudate and drusen patches and used to build a
Grinsven et al. REF have proposed to use the BOW approach to retrieve and classify images with bright lesions, namely drusen and exudates.
A Bag of Words approach for discriminating between retinal images containing exudates or drusen
2013 IEEE 10th International Symposium on Biomedical Imaging
2013 IEEE 10th International Symposium on Biomedical Imaging
Computer Science