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Fundus Foveal Localization Based on Image Relative Subtraction
Fundus Foveal Localization Based on Image Relative Subtraction-IReS Approach Jeetinder Singh Jayanthi Sivaswamy International Institute of Information Technology International Institute of Information Technology Hyderabad,India Hyderabad,India Email: firstname.lastname@example.org Email: email@example.com Abstract—In this paper we present a method for fovea localiza- tion which does not use organization information of other retinal structures like optic disk and arcades. The main advantage of this method is that it does not require segmentation/localization of other retinal structures which are required as prior knowledge in existing fovea localization methods. The key idea is to enhance the relative contrast between the fovea and its surrounding such that it is well-separated in a retinal image. The approach was tested on 520 retinal images which include normal and pathological color retinal images. An overall accuracy of 90.57% is reported independent of left eye, right eye, normal or pathological retinal images. (a) (b) I. I NTRODUCTION Fig. 1. (a) Retinal image with marked fovea rgion (b) Gray channel image Fovea is a key region which is responsible for high acu- ity colour vision. Hence, the location of lesions relative to fovea, is of interest in diseases such as age-related macular degeneration or diabetic retinopathy. This information is used used to locate the fovea. OD is localized using principal com- by ophthalmologists for correct diagnosis and treatment. In ponent analysis and modiﬁed active shape model is applied general, estimation of the fovea shape and localization helps in to extract the main courses of blood vessels. Localization of establishing statistics regarding position of lesions for disease the fovea is done based on its geometrical relation to OD and gradation and provide a better description of other features blood vessels. The approach showed a sensitivity of 100% on in fundus images. Accordingly, fovea detection forms a com- 35 images. A similar strategy  was employed based on ﬁrst ponent of most systems that are designed for automatically identifying the main blood vessels using the modiﬁed active analysing retinal images. The challenge in fovea detection is shape model. The candidate region is determine by spatial that its size and/or position can vary with the chosen ﬁeld relationship of the OD and the parabolic structure of blood of view (macula centric or optic disk centric), magniﬁcation vessels. The darkest region is searched within candidate region level, non-uniform illumination; the fovea can also suffer from to identify fovea. partial or full occlusion due to pathological factors such as In  approach is design based on a priori knowledge of lesions, scars etc. The existing approaches employed for fovea OD position and its relative distance from fovea. Template was localisation use geometrical relation to other structures to deﬁned to approximate a typical fovea. The fovea is identiﬁed locate the fovea. based on the location of maximum correlation between the A method  is designed based on priori knowledge of template and the intensity image in HSI space. The variance the optic disk (OD) position . The OD is localized based of intensity of adjacent pixels was used to locate OD. The on pyramidal decomposition and Hausdorff-distance based method was tested on a total of 112 retinal images divided template matching. The candidate regions for OD are ﬁrst into 3 groups and detected fovea in 78 images. found based on pyramidal decomposition on the grayscale In  , a system closely related to  is designed to green plane of the original image. Candidate regions are determine the landmark positions by ﬁtting a single model to looked for the presence of circular templates using Hausdorff- the image. The algorithm requires two sub models. One is a distance as a distance measure. The OD is identiﬁed based shape model and second model is an energy function. A set of on center of the ﬁtted circular template. Macula distance with 16 points were chosen to deﬁne shape model known as a point respect to the OD is used as a priori knowledge to position distribution model. The algorithm ﬁnds the shape or landmarks fovea. The method reported 95% accuracy on 40 images. that minimize energy function. A number of different system A model based approach is proposed  in which the OD setups are compared. The approach reported 94.4% accuracy location and extraction of the main courses of blood vessels is on the 500 images of the screening test set and 92.0% on the to be the required plane Ia as it contains the fovea (which appears as a dark region) embedded in clutter made up of vessels (which also appear dark), optic disk (bright region) and other pathologies. These characteristics can be observed in the gray level intensity plane of image in Fig.1,(b). It can be observed from this image that the contrast of the foveal region (a) (b) (c) is not very high as the overall appearance of the intensity Fig. 2. Few samples fundus images with pathology plane is on the dark side. Alternatively, the signal of interest is biased with a low intensity background illumination which itself can be slowly varying across the image. This observation serves to guide the choice for the image plane Ib . A suitable 100 pathological test images. choice for Ib should contain the foveal region in addition The goal of this paper is ﬁrst develop a method for fovea to providing information about the background illumination. localization without prior knowledge of other features. Sec- The red plane image which is shown in Fig.4(a), is a good ondly, the algorithm is analyzed and tested for detection of candidate for Ib . The red plane’s visual appearance indicates fovea region. The approximate size of the visible fovea region that the dominant component is the illumination function in a ﬁnal output image can also be obtained. (bright and slowly varying). This point has been exploited to We present an algorithm for detecting the fovea which is perform illumination correction, via histogram matching with based on the following domain knowledge: fovea is the darkest the green channel image, prior to vessel detection in . Other region within the macula in a colour retinal image with a attractive features of the red plane for our problem is that distinguishing feature namely, the absence of blood vessels it contains the fovea as a low contrast region in addition to (Fig.1). The degree of darkness and size depends on factors another component of the clutter, namely, the vessel structure such as uneven illumination, presence of lesions such as drusen (which share similar characteristic as the fovea in the image and exudate’s. The proposed algorithm is based on difference Ia ). Hence, by combining the two gray level intensity and red of two image planes by modeling the fovea detection as one plane images it should be possible to extract the foveal region. of signal detection from clutter. The outcome of the proposed The strategy for combining should ensure that the relative detection consists of top two candidates for fovea. An effort contrast of the foveal region is strengthened while that of other has been made to cope with retinal images distorted by the structures are weakened. The proposed algorithm is based on presence of lesions etc. difference of two image planes by modeling the ROI as a dark The paper begin with brief discussion devised a very region in given input image and deﬁned as: basic deﬁnition of relative subtraction to describe the fovea localization algorithm in section II. In section III, we discuss Is = Ia − (Ib )c (1) fovea localization approach. And ﬁnally will conclude with experiment results and a brief discussion. Where Is is Ia ∩ Ib , set which contain common element of Ia and Ib . II. R EGION OF I NTEREST (ROI) AGGREGATION BASED ON We will now show the operations that are required to achieve I MAGE R ELATIVE S UBTRACTION - IR E S this goal. The Fig.1,(a) shows a sample colour image taken with 1) Selection of Ia and Ib : In image Ia the (ROI) is em- a macula-centric ﬁeld of view. It can be observed that the bedded. The acquisition of image Ib should be designed fovea is a dark and roughly homogeneous region. However, such that it contains the (ROI) in addition stretch the the relative contrast between the fovea and its surround can brighter region over wider range without distorting the decrease with an increase in the magniﬁcation level. This can ROI characteristic. be seen from ﬁg.2. From a signal detection perspective, fovea 2) Calculate Is = Ia − (Ib )c detection can be viewed as a problem of detecting the signal of 3) Apply threshold T as deﬁned distance measure for ﬁl- interest (fovea) from a given colour image where the signal is tering plausible candidates for ROI . embedded in clutter. The clutter component represents all the 4) Apply prior knowledge to correctly identify the object background structures as well as the non-uniform illumination ROI. in the image. Let us consider projecting the image into two Theoretically step 2 will enhance common regions in image appropriate planes Ia and Ib such that both Ia and Ib contain Ia and Ib . If ROI, in our case fovea, share the same charac- the signal of interest. Additionally, let Ib capture the clutter teristics in all three grayscale image channels , the selection to a large extent. Subtracting Ib from Ia will remove the of two grayscale image channels will ensure the enhancement clutter from Ia but will also weaken the signal. If instead of ROI. The approach is simple and works if object Ir can be we complement Ib and do a relative subtraction operation, the associated with unique characteristic to ﬁnally ﬁlter it from the signal gets enhanced while the clutter is suppressed. result of subtraction. Fig.3 illustrates the ﬂowchart of proposed In the context of fovea detection, the plane that contains algorithm. Illumination corrections and contrast enhancement achromatic input, namely the intensity plane can be taken are required in the presence of variety of retinal diseases. Fig. 3. IReS algorithm (a) (b) (c) Fig. 4. Three component of color image: (a) Red Channel (b) Green Channel (c) Blue Channel (a) (b) (c) Fig. 5. Each row shows result of selection of three different Ib images expressing properties of Eq.1, where Ia in each row is gray channel image. The images Ib in each row are: red channel, green channel, blue channel from top row to bottom. The gray scale distribution of image Ib in each row was III. F OVEA L OCALIZATION BASED ON R ELATIVE described by combination of local adaptive contrast enhancement and linear S UBTRACTION A PPROACH contrast stretching function window (low-in,high-in,0,1). A. Selection of two images The proper selection for image Ia and Ib is essential such that in later stages we obtained a enhanced grayscale image in which ROI gets maximum contrast compare to other retinal structures. In Fig.5 experiment analysis of Eq.1 o clearly illustrates the effect of choosing the different Ib . The image Ia is chosen as contrast enhanced gray channel in RGB space where the (ROI) is embedded. Theoretically the (a) (b) image Ib image should contain the (ROI) as dark region as foreground and rest of the retinal image as bright background. Fig. 6. (a) Enhanced resultant gray scale image. (b) Contrast enhancement of the the image shown in Fig.6 (a) using iterative contrast enhancement The main idea behind the approach is that the image Ib approach. should contain only the (ROI) foveal region in addition to the background illumination (Fig.5). The red channel image is nor- mally brighter and captures the maximum noise in the retinal image, (Fig.4(a)). Initial evaluation showed that approximation B. Relative Subtraction of Images of contrast enhanced red channel image as Ib in RGB space In Fig.5 experiment analysis of Eq.1 clearly illustrates perform better in comparison with other color plane images. that after subtraction of two images low value pixels are The uneven illumination and certain abnormalities effect the enhanced and get distinguished irrespective of its background contrast between fovea and background. Thus, in order to and global threshold is capable of separating the ROI from its compensate for uneven illumination, contrast enhancement are background. But in the retinal images there are other dark required on the gray channel and the red channel images. The region and as a result selection criteria can easily fail in gray channel image is darker,(Fig.1(b)), and fovea is not well pathology effected images. Next to overcome this challenge separated from its background. The preprocessing is required gradient method are evaluated. to separate the fovea from its background without effecting Gradient based methods can easily enhance heterogeneity in the fovea region. Hence a linear contrast-stretching function the retinal image due to the vessels. Other regions in retinal is applied to separate the fovea from its background. But the image can be looked as non homogeneous region in the sense red channel image of a retinal image tends to be saturated and that blood vessels are ﬂowing through these regions. Enhance- visually bright. To enhance the details of local darker regions ment of non-uniformity will separate the non homogeneous local adaptive contrast (CLAHE) is applied. and homogeneous regions. But gradient based method will without affecting the fovea region. A sensitive algorithm needs to be designed to identifying the fovea in the presence of heavily confounding features. The top-hat has advantage over complement in the presence of uneven noise and dark regions at periphery. The ﬁg.8 shows schematic representation of modiﬁed and improved IReS algorithm for fovea localization. IV. E XPERIMENT R ESULTS AND D ISCUSSION Fig. 7. Two-stage contrast enhancement and global thresholding procedure The evaluation of the proposed algorithm was performed using the public database DRIVE  , STARE  and LVPEI . The images in LVPEI dataset were acquired using a Zeiss reduce the contrast between between fovea and background digital fundus camera with a 50 degree ﬁeld of view (FOV). when compared to complement operation. The subtraction Each image was captured using 8 bits per color plane at 768 after top-hat morphological operation on red channel image by 576 pixels. (Fig.6(a)) shows how non homogeneity has been brought The total number of images taken from each dataset are into image using top-hat operation near periphery. The top- given in table table I. Each database was divided into two hat has advantage over others as it exploits the circular nature groups, level-1 and level-2 irrespective of macula centric or of fovea and feature of ROI is preserved without distortion. non macula centric. Level-1 images have a lot of variation in Also as a result of subtraction of images the resultant enhanced contrast, noise, fovea size and illumination. Level-2 are images gray-level image (Fig.6(a)) has well-separated smooth regions affected by drusen, fovea region lie near to retinal boundary, sharing some common trait which can be clustered together. size of fovea is small compared to other darker region with The contrast between regions need to be improved to separate variation in illumination and contrasts. Images without fovea the regions. region or almost negligible fovea were not considered. The adaptive histogram-based segmentation technique en- We report the success of our method to localize the fovea hances cohesive regions and segment areas that slightly differ using modiﬁed IReS approach alone, without prior knowledge from their background regions. CLAHE is more effective and and relationship of other structure in retinal image. The works well for both global and local variations (Fig.6(b)). An method has been evaluated on total of 520 images taken from iterative local contrast enhancement technique performs very DRIVE, STARE and LVPEI. The overall accuracy for the well in cases of large intensity variations. A similar approach fovea detection is 90.74%, I. The results shows overall fovea has been taken in . A two-stage contrast enhancement detection accuracy of 92.74% on Level-1 dataset and detection (Fig.7) method is developed which is more effective than accuracy of 88.37% on Level-2 dataset. The results have been iterative local contrast enhancement technique or single pass visually veriﬁed by physician (few results images are shown contrast enhancement. The single pass stage applies the global in ﬁg 9). threshold to all images but it fails to reconstruct fovea region The method fails in cases: 1)if fovea region fails to satisfy in case of extreme intensity variation. But iterative pass stage maximum sized dark region selection constraint 2) cases where tries to enhance and reconstruct fovea where single pass the red channel is highly saturated and fails to capture fovea stage fails. The histogram equalization (HE) and CLAHE are region . The optic disk centric, high saturated red channel main method of enhancement. Although they are simple to images in STARE database lower the method performance implement and unsupervised this can result in some minor ((Fig.10). inaccuracies, particularly if any other region is enlarged com- pared to ROI. Category Level-1 Level-2 Total Fovea Detected Fovea Detected Accuracy C. Detecting the Best Candidate for Fovea Region DRIVE 100% (35/35) - 100% (35/35) STARE 79.48% (31/39) 87.80% (36/41) 83.75% (67/80) As a result of improvement in the contrast due iterative local LVPEI 94.14% (177/188) 88.47% (192/217) 91.11% (369/405) enhancement technique fovea region also get enhanced which Total 92.74% (243/262) 88.37% (288/258) 90.57% (471/520) helps in choosing a global constant threshold. The pixels above a normalized intensity of 80% in gray scale intensity image are TABLE I selected. Even after binarization the main obstacle is removal P ERFORMANCE EVALUATION OF THE FOVEA LOCALISATION . of other plausible candidates in extreme intensity variation and the presence of retinal blood vessels. Since vessels are like long threads, this property is used to distinguish between the V. C ONCLUSION fovea from vessels. Finally the region with maximum area A new approach has been proposed to extract fovea region is marked as plausible candidate with maximum conﬁdence automatically and robustly in color fundus images without level. prior knowledge of other landmarks. The approach is useful for The main difﬁculty in applying complement is removing independent retinal image processing tools. The approximation the inﬂuence of blood vessels and retinal image boundary of visible fovea region can provide better feature description. Fig. 8. Schematic representation of modiﬁed IReS approach for fovea region detection algorithm, Top-hat operator exploit the heterogeneity in the retinal image due to the vessels. (a) (b) (c) (d) (e) Fig. 9. Few samples of successful results on level-1 and level-2 dataset images. anatomical landmarks as well. R EFERENCES  L. Gagnon, M. Lalonde, M. Beaulieu, and M. 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"Fundus Foveal Localization Based on Image Relative Subtraction"