Red Lesions Detection in Digital Fundus Images - Semantic Scholar

RED LESIONS DETECTION IN DIGITAL FUNDUS IMAGES S. Balasubramanian, Sandip Pradhan, V. Chandrasekaran Department of Mathematics and Computer Science, Sri Sathya Sai University, Prasanthi Nilayam, India ABSTRACT In this paper we propose a novel method for automatic detection red lesions in digital fundus images. Candidate red lesions are extracted by a novel method called Automatic Seed Generation (ASG). For classification, an implicitly hybrid classifier called spatio temporal feature map classifier (STFM) has been employed. Inclusion of a new feature called elliptic variance during classification phase has significantly reduced the false positives. The hybrid classifier reports 87% sensitivity and 95.53% specificity.

estimated using an implicitly hybrid STFM classifier and a set of features. 2. RETINAL IMAGE PREPROCESSING Gray level grouping (GLG) based contrast enhancement and shade correction through median filtering is performed at this stage on the green channel of the input image. We denote the estimated background image by Ibg and shade corrected image by Isc . Figure 1 portrays a sample result.

Index Terms— Retinal image analysis, Automatic Seed Generation, Microaneurysm, Elliptic Variance, STFM. 1. INTRODUCTION Automatic detection of red lesions is important in relation to quantification of Diabetic Retinopathy(DR) in diabetic patients. In [1] - [3] morphological top hat transform (MTH) has been applied for eliminating the blood vessels from fundus images. The residual regions were considered as candidate microaneurysms (MA). Region growing algorithm was employed for the MA detection from the candidates. It was further improvised using Watershed retinal region growing and contrast normalization to improve the ability to distinguish MA from other dots that occur on the retina [3]. The authors reported a sensitivity of 85.4% and specificity of 83.1% respectively. Walter et al.[4] exploited bounding box closing for the detection of dark details in the gray level images. A diameter criterion was then used to eliminate all holes with a diameter smaller than a constant λ. A sensitivity of 74.8% on a database of 5 images was mentioned in [4]. Meindert et al. [5] used pixel classification based on supervised learning to separate the vasculature and red lesions from the background. An extensive number of new features were then added to those proposed by Spencer et al [1]. A sensitivity of 100% and specificity of 87% using kNN classifier was reported on a database of 100 images. In this paper we propose a simple and effective method for the detection of red lesions in digital fundus images. Following contrast enhancement and shade correction, candidate lesions are localized by ASG devoid of MTH. Subsequently the probability for each candidate to represent a red-lesion is

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(a)Image in Green Plane

(b)After applying GLG

(c) Shade Corrected Image Fig. 1. Different stages of preprocessing

3. CANDIDATE RED LESION DETECTION BY AUTOMATIC SEED GENERATION Two set of pixels in Isc namely foreground pixels and background pixels are differentiated. Foreground pixels include red lesions and background pixels include the rest. Since blood vessels have intensity values similar to red lesions they are grouped under foreground pixels. They can be later removed using length criterion. A pixel p is called as seed pixel or candidate pixel if it satisfies the following criterion in order: Pixel p is a foreground pixel. Pixel p is similar to its neighbors. Each connected component of seed pixels is taken as one seed or in other words, a candidate red lesion. A pixel p is identified as foreground pixel if it satisfies (1)

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and (2): Isc (p) = tIbg (p). (1) The first inequality in (1) ensures that pixels higher than corresponding background, for example bright lesions, are not identified as foreground pixels. The constant t ∈ (0, 1) in the second inequality is a control parameter that controls the sensitivity of candidate detection. As t approaches zero, the number of candidates detected increases and vice versa. The similarity of a foreground pixel to its neighbors is obtained by considering a 3x3 neighborhood and computing the similarity measure S =1−σ

(2)

9 1 In (2) σ = ¯)2 with xi = |Ibg (i) − Isc (i)| i=1 (xi − x 9 9 and x ¯ = 19 i=1 xi . The motivation behind choosing this similarity measure is that a foreground pixel is similar to pixels in its neighborhood only if they are also foreground pixels. Hence a seed pixel or a candidate pixel must be a foreground pixel and have its similarity measure greater than a threshold value T. Figure 2(a) depicts a result of ASG for the preprocessed image in Figure 1 (c). Note that vessel pixels also belong to foreground set as can be observed from the result. Since red lesions do not appear on larger vessels, they are disconnected from the vasculature. Hence all seeds whose diameter is greater than a diameter threshold D disqualify to be candidate red lesions. In this way objects like blood vessels which are too large to be red lesions are removed as depicted in Figure 2(b).

(a)Image after ASG

(b)vessel pixels removed

vectors, initialized randomly, stabilize as a result of topological ordering effected by Self Organizing Maps. The second stage has the neuronal gate and involves N-dimensional spatial grating modulation and neuronal selection for competition at the third stage. This neuronal gate is controlled by a periodic function. We have used a cosine function with a time dependant monotonically decreasing frequency and the distance between the input pattern and the weight vector as its arguments as shown in (4). ON ctrl () > 0 gate (ctrl (.)) = (3) OF F otherwise where ctrl (.) = cos (2πf (t) d (.)) where fmax 1 − f (t) = 0

4. CLASSIFICATION OF RED LESIONS For Classification we have considered the feature set proposed in [5]. Subsequent to normalization of the feature set classification is performed by STFM. 4.1. STFM The STFM with gated neuronal architecture is diplayed in figure 3. It has three functional stages. The first stage computes the distance between the input pattern and all the weight vectors associated with the neurons in the lattice. These weight

t T

t≤T otherwise

(5)

In (4) the distance d (.) is the value of the distance measure obtained at each neuronal output at the first stage and fmax defines the maximum spatial grating frequency. When the gate control switches ON the neuronal gate, the associated neuron with its distance measure competes in the next stage. The third stage is a ”winner take all” which identifies the neuron having the least distance and declares it as the winner. This stage provides the spatio-temporal signature for each input pattern. The output side of each neuron is provided with a winning event register which flags at every winning instance. From each of these winning instances, connections are made to class nodes. If ni (α, β, t) denotes the number of patterns at any time instant t for class i mapped on to the neuron indexed by (α, β) and n (α, β, t) the total number of patterns of all classes at the same time instant t, we obtain the class probability density as: P (Hi | (α, β)) =

Fig. 2. Candidate Red Lesion Detection using ASG

(4)

ni (α, β, t) n (α, β, t)

(6)

where Hi is the hypothesis that the input pattern belongs to class i. For final classification we can simply add the probability densities of each class at each winning nodes as the spatio-temporal signature is generated and then decide on the class label having the maximum total class probability values. Since the values of these sums could exceed 1.0, these are often refered to as strengths of belief. The following analogy in relation to diagnosis by human experts can be drawn to STFM. A single expert may give opinions with varying degrees of confidence on the same data at different instances of time. However, the optimal strength of belief for the particular disease would be an aggregate of strengths of beliefs at different instances of time. It is as if

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5. MATERIAL, EXPERIMENT, RESULTS AND DISCUSSIONS

Fig. 3. STFM using gated neuronal architecture the same photograph is analysed by multiple experts who arrive in consensus for the presence of the disease. So too is the behaviour of STFM. Hence STFM is implicitly hybrid in nature. 4.2. ELLIPTIC VARIANCE As suggested in [1] by analysis of histograms of features relating to size and shape data, larger, linear and more complex objects can be easily discriminated from compact, circular red lesions whose sizes fall between well-defined limits.This motivated us to use elliptic variance [9] for better discrimination. It is defined as follows: Let the contour of a candidate region, after ASG be denoted by R = {ri }, where ri = (xi ,yi ), i = 1, . . . , r. Then MR = r r 1 1 r is the centroid and m = i R i=1 i=1 ri − MR is r r the mean radius . Elliptic variance is defined as evar =

1 ( (ri − MR )T C−1 (ri − MR )−MRC )2 rMRC i (7)

where MRC =

1 r i

C=

(ri − MR )T C−1 (ri − MR )

1 (ri − MR )(ri − MR )T r i

(8)

(9)

The inclusion of elliptic variance has drastically reduced the false positive rate across the classifiers we have tested as shown in the Table 1.

Our database consisted of 63 images. We divided it to two sets - Set 1 of 30 images for training and Set 2 of 33 images for testing the classifiers. The database has been annotated by an expert ophthalmologist. The parameters used for running the proposed method are: [m, n] = [57, 57]; t = 0.10 and T = 0.95; D = 60; k = 50 in kNN; no of Gaussians = 4 in GMM. We implemented the proposed approach in MATLAB (version 7) on a Pentium IV. We compared our method with the one proposed in [4]. Though this method is proposed for MA detection, we amended this for red lesion detection by adjusting the diameter parameter. Table 2 clearly demonstrates the superiority of our ASG candidate extraction algorithm in terms of number of true red lesions missed out compared to [4]. Further ASG has eliminated a good number of potential false positives as early as in candidate extraction step (see column 2 in Table 2). Performance evaluation for classification is based on sensitivity, specificity and predictivity criterion. Table 3 displays the values of these metrics for all the classifiers we have tested. Though GMM has higher predictivity (86%) than STFM, it is only STFM that scores the best sensitivity (83.57%) with a significant predicitivity (81.32%). Screening systems always require higher sensitivity than other metrics though the number of false positives should not be significantly high. The effectiveness of elliptic variance as a shape descriptor in discriminating red lesions from non red lesions was discussed in Section 4 and its effectiveness was amply demonstrated in Table 1. Table 4 depicts the dominance of our method over [4] in terms of sensitivity, specificity and precision. A sample of our results is shown in Figure 5 . 6. CONCLUSION In this paper we have proposed a novel method for red lesion classification in digital fundus images. The superiority of ASG candidate extraction step, the effectiveness of elliptic variance feature and the strength of STFM was demonstrated using suitable performance metrics like sensitivity, specificity and predictivity. Sensitivity measuring 83.57% suggests that the method could be used in screening setting. Classifier kNN GMM SVM LMSE Hybrid

% of False Positives Removed 73% 66.6% 74.0% 13.5% 78.8%

Table 1. Effect of Circular Variance


Methods Method in ASG Method in Bounding Box [4]

No. of Candidates extracted 693

True red lesions missed out 65

1818

95

Table 2. Performance of the ASG and the method in [4] on a database of 30 images. (a)Without evar

(b)with evar Classifier kNN GMM SVM LMSE STFM

Fig. 4. The effect of elliptic variance on removing false positives

Sensitivity 82.14 70.23 61.90 55.95 83.57

Specificity 99.29 99.54 99.59 99.69 99.15

Predictivity 69.0 86.0 72.0 59.0 81.32

Table 3. Performance of Classifiers

(a)Input Image1

Method Proposed Method[4]

(b)Output Image1

Sensitivity 87.0 72.61

Specificity 99.53 99.39

Predictivity 79.0 72.6

Table 4. Comparison to [4] [5] Meindert Niemeijer, Bram van Ginneken, Joes Staal, Maria S. A. Suttorp-Schulten, and Michael D. Abrmoff, ”Automatic Detection of Red Lesions in Digital Color Fundus Photograph,” IEEE Transactions on Medical Imaging, vol. 24, no. 5, pp. 584592, May 2005. (a)Input Image2

(b)Output Image2

[6] Zhi Yu Chin, Besma R.Abidi, David L.Page and Mongi A.Abidi, ”Gray-Level Grouping (GLG) : An Automatic Method for Optimized Image Contrast Enhancement Part I : The Basic Method,” IEEE Transactions on Image Processing, vol. 15, no.8, pp. 2290-2302, August 2006.

Fig. 5. Detection of Red Lesions 7. REFERENCES [1] T. Spencer, J. Olson, K.McHardy, P. Sharp, and J. Forrester, ”An image processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus,” Comput. Biomed. Res., vol. 29, pp. 284302, 1996. [2] M. Cree, J. Olson, K.McHardy, P. Sharp, and J. Forrester, ”A fully automated comparative microaneurysm digital detection system,” Eye, vol.11, pp. 622-628, 1997. [3] A.D. Fleming, S.Philip, K.A. Goatman, J.A.Oslon, and P.F. Sharp, ”Automated MicroAneurysms Detection using Local Contrast Normalisation and Local Vessel Detection,” IEEE Transactions on Medical Imaging, vol.25, no.9, pp. 1223-1232, September 2006.

[7] J.V.B. Soares, J.J.G. Leandro, R.M. Cesar-Jr, H.F. Jelinek, and M.J. Cree, ”Retinal vessel segmentation using 2-D Gabor wavelet and supervised classification,” IEEE Transactions on Medical Imaging, vol.25, pp. 1214-1222, September 2006. [8] V. Chandrasekaran, ”Gated Neural Networks For 3D Object Recognition systems,” Phd Thesis, University of Melbourne, 1995. [9] M. Peura, and J. Iivarinen, ”Efficiency of simple shape descriptors,” Proceedings of the Third International Workshop on Visual Form, Capri, Italy, May, 1997, pp. 443-451.

[4] Thomas Walter, and Jean Claude Klein, ”Automatic Detection of MicroAneurysms in Color Fundus Images of Human Retina by means of the Bounding Box Closing,” Proceeding of Medical Data Analysis, 2002, pp. 210-220.
