Multi-parametric optic disc segmentation using

Expert Systems With Applications 120 (2019) 461–473

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Expert Systems With Applications journal homepage: www.elsevier.com/locate/eswa

Multi-parametric optic disc segmentation using superpixel based feature classificationR Zaka Ur Rehman a, Syed S. Naqvi b,∗, Tariq M. Khan b, Muhammad Arsalan c, Muhammad A. Khan d, M.A. Khalil e a

Department of Computer Science and IT, The University of Lahore, Gujrat campus, Gujrat, Pakistan Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad Campus, Pakistan Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul, 100-715, Korea d School of Computing and Communications, Lancaster University, Lancaster, UK e Department of Computer Engineering, University of Lahore, Lahore, Pakistan b c

a r t i c l e

i n f o

Article history: Received 26 April 2018 Revised 3 December 2018 Accepted 4 December 2018 Available online 4 December 2018 Keywords: AdaBoostM1 Glaucoma RusBoost Random forest Support vector machine

a b s t r a c t Glaucoma along with diabetic retinopathy is a major cause of vision blindness and is projected to affect over 80 million people by 2020. Recently, expert systems have matched human performance in disease diagnosis and proven to be highly useful in assisting medical experts in the diagnosis and detection of diseases. Hence, automated optic disc detection through intelligent systems is vital for early diagnosis and detection of Glaucoma. This paper presents a multi-parametric optic disk detection and localization method for retinal fundus images using region-based statistical and textural features. Highly discriminative features are selected based on the mutual information criterion and a comparative analysis of four benchmark classifiers: Support Vector Machine, Random Forest (RF), AdaBoost and RusBoost is presented. The results of the proposed RF classifier based pipeline demonstrate its highly competitive performance (accuracies of 0.993, 0.988 and 0.993 on the DRIONS, MESSIDOR and ONHSD databases) with the stateof-the-art, thus making it a suitable candidate for patient management systems for early diagnosis of the Glaucoma. © 2018 Elsevier Ltd. All rights reserved.

1. Introduction Nowadays, people are unaware of the visual impairment and blindness lesions of an eye that are macular degeneration, hypertension, glaucoma, and diabetic retinopathy (Lee, Wong, & Sabanayagam, 2015; Soomro et al., 2018). Glaucoma is an incurable eye pathology around the optic disc (OD) that badly damages the optic nerves and leads to vision loss. Once started, it cannot be cured but can be prevented by diagnosing at its early stage (Soomro, Gao, Khan, Hani, Khan, & Paul, 2017; Soomro, Khan, Gao, Khan, & Paul, 2017). According to an estimate, it becomes the most crucial and leading cause of vision blindness, it may affect about 80 million people by 2020 (Khan, Khan, Bailey, & Soomro, 2018; Khan, Khan, Soomro, Mir, & Gao, 2017; Quigley & Broman, 2006).

R

An Explicit Method for Localization of Optic disc from Retinal fundus Images. Corresponding author. E-mail addresses: [email protected] (Z.U. Rehman), [email protected] (S.S. Naqvi), [email protected] (T.M. Khan), [email protected] (M. Arsalan), [email protected] (M.A. Khan), [email protected] (M.A. Khalil). ∗

https://doi.org/10.1016/j.eswa.2018.12.008 0957-4174/© 2018 Elsevier Ltd. All rights reserved.

The progression rate of this disease is slow but occurs gradually over an extended time interval. The symptoms of glaucoma appear after an extensive time period when the disease is quite advanced. It is incurable but the growth rate of its progression rate have been regressed by proper treatment if it is diagnosed at its primary stage. The glaucoma progression is symptomized from the few signs of vision loss. In Australia, 50% of peoples are unfamiliar with glaucoma (Maldhure & Dixit, 2015). The OD is defined by its central bright region called optic cup (OC) and the corresponding outer peripheral region called the neuroretinal rim, as shown in Fig. 1. The cup to disc ratio (CDR), which is the ratio of vertical cup diameter (VCD) to vertical disc diameter (VDD) is considered as a vital factor in glaucoma screening (Almazroa, Burman, Raahemifar, & Lakshminarayanan, 2015), due to its directly proportional relationship with glaucoma occurrence. The segmentation of the OC has been demonstrated to rely on accurate localization of the OD in literature (Yin et al., 2012). On similar lines, in this work, we argue that the correct localization of the OD is vital for eye pathology screening and diagnosis. Expert and intelligent systems continue to contribute towards scientific and technological advances in numerous fields from med-

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Fig. 1. Optic disc and optic cup main structures. The enclosed region with green circle is for optic disc, central region enclosed with blue circular is the optic cu p. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

ical applications, telecommunications, economics, transportation, and surveillance. An important milestone for intelligent machine vision systems is to match or surpass human vision performance. Expert automated vision systems have been successfully applied to disease diagnosis: recent works include real-time detection of tuberculosis using an intelligent mobile-enabled expert system (Shabut et al., 2018), deep neural network based recommender system for skin lesion extraction (Soudani & Barhoumi, 2019) and a convolutional neural network based segmentation framework for breast tumor classification (Rouhi, Jafari, Kasaei, & Keshavarzian, 2015). The multifaceted challenges in OD localization include deformable shape, variation in color, OD boundary discontinuities due to blood vessels and glaucoma pathology such as peripapillary atrophy (PPA) (Jonas, Fernández, & Naumann, 1992), and ISNT rule (Harizman et al., 2006). Most of the unsupervised and supervised methods for OD boundary detection are gradient dependent approaches. Hence, they struggle to capture true OD boundary in cases where peripapillary atrophy (PPA) makes the OD boundary discontinuous or the vascular structure originating from the OD could misguide the segmentation. Also, the supervised approaches for OD localization (Fan et al., 2018) typically give less attention to important stages of the learning pipeline, including preprocessing, appropriate feature selection and data imbalance problem in medical images. To this end, we proposed a supervised approach for OD localization which is independent of boundary information, the deformable shape of the OD and the irregularities in its size. To enhance the OD region and suppress the vessel background, appropriate image preprocessing is applied to increase the generalization ability of the classifier. The proposed method utilizes the statistical and textural properties of regions to learn a discriminative classifier from positive and negative regional samples. Pixel-accurate ground truth annotations are employed to generate training instances. In this work, various discriminative classifiers are trained and the Random Forest classifier is selected as the proposed approach due to its better generalization performance. The proposed method can robustly localize OD with high accuracy, which makes it a suitable candidate for glaucoma screening and diagnosis. The specific contributions of this work are: 1. The problem of OD segmentation is modeled as a regional classification problem through a systematic classification pipeline. 2. Image regions are characterized by statistical as well as textural properties in a well-formulated classification framework to make it robust against multifaceted challenges in OD localization. 3. A thorough comparison of regional classifiers is presented for OD localization in retinal fundus images. Our results demonstrate the improved generalization of the Random Forest clas-

Fig. 2. Typical challenge in OD localization from retinal fundus. Green circular lines: experts mark OD boundary; blue circular lines: represents the OD by Yin et al. (2011) and Cheng et al. (2011) in both of the examples; skyblue lines mark disease around OD, where one of them is the PPA boundary. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

sifier over other classification based methods and unsupervised counterparts. The rest of this paper is organized as follows. A detailed review of the relevant methods is presented in Section 2. The proposed method’s main steps for OD localization are presented in Sections 3–8 that comprise of the preprocessing, superpixel segmentation, feature extraction and selection and the classification of each superpixel by using machine learning tools. Sections 9 and 10 comprise of experimental setup that contains datasets description, parameter selection, and comparative experimental results. The discussion and conclusions are described in Sections 11 and 12, respectively. 2. Literature review Computerized OD localization and segmentation is still considered as a highly challenging task and an open problem in medical image analysis and diagnosis. The obvious challenges include the ever-varying physical characteristics of OD (such as size, structure, vessel structure, color) and various glaucoma pathology, as shown in Fig. 2. Considering only the independent scanning protocols, all of the mentioned properties are unknown. Many OD and eye lesion detection algorithms have been developed for 2D retinal fundus images. The current state-of-the-art techniques on OD localization and segmentation can be categorized into two categories: unsupervised and supervised approaches. Morales, Naranjo, Angulo, and Alcañiz (2013) reviewed the unsupervised based OD segmentation methods in three ways, namely template based (Lalonde, Beaulieu, & Gagnon, 2001; Park, Jin, & Luo, 2006), deformable model based (Lalonde et al., 2001; Lowell et al., 2004; Morales et al., 2013), and morphology-based (Morales et al., 2013; Walter, Klein, Massin, & Erginay, 2002; Welfer et al., 2010). All these methods finally mark the disc boundary by circular Hough transforms due to its computational efficiency. The circular ellipse is fitted for glaucoma screening. However, all these methods are unsupervised. Some other methods that are pixel classification based methods (Maninis, Pont-Tuset, Arbeláez, & Van Gool, 2016b; Welfer et al., 2010) and hybrid soft computing based methods are also used for OD segmentation. In the template-based matching methods, Park et al. (2006) proposed the OD segmentation by thresholding followed by the circular Hough transform. The region with the highest average intensity indicates the OD. The threshold-based

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approach involves many parameters including the thresholds, which cannot be suited to all images. Also, the highest intensity assumption can be misleading in pathology images or in screening. Lalonde et al. (2001) adopted canny edge detector to extract the boundaries of OD and Hausdorff template matching is used to fit the OD boundary by a circle. Although, the results were promising, however, the suitability of the thresholds for the edge detection process for a wide variety of images is questionable. Lowell et al. (2004) proposed a deformable model-based method for OD segmentation by employing the image gradient based optimal points on the active contour of the image edges. Although, it was the first most comprehensive method for OD localization and segmentation, however, the deformable model is sensitive to noise due to peripapillary atrophy and pathologies. A morphology-based method was proposed by Walter et al. (2002) that extracts OD using watershed transformation. This method is based on a superfluous assumption that the OD is usually represented by bright pixels in fundus images, which stands invalid for pathology and real-life screening images. In Morales et al. (2013), the gray image obtained by PCA is chosen as the input. The stochastic watershed is applied to extract the watershed regions. Afterward, region discrimination is performed to select the pixels which belong to OD based on the average intensity of the region. The process of region discrimination on the basis of the average intensity of regions in the watershed segmentation step could fail in scenarios where OD boundaries are not well-defined, as in case of Glaucoma affected images. Also, the initialization of the markers is crucial for the efficient working of the approach proposed by Morales et al. (2013). Welfer et al. (2010) proposed an adaptive method for the segmentation of the OD using an adaptive morphological approach. They used the particular attributes of the OD to address the issue including solid distracters along the vessel edges and peripapillary decay. Although, the approach was promising, the overall performance was disproportionate due to the deformable arbitrary shape OD regions obtained as the output of the watershed segmentation step. A supervised method for OD segmentation based on structured learning is proposed recently by Fan et al. (2018). This method trains a structured forest for OD edge detection that is based on the Random Forest classifier. The OD edges obtained by the trained structured forest are thresholded, post-processed and a circular Hough transform is applied to obtain the OD boundary. This is a competitive approach for OD detection and achieved performance comparable to the state-of-the-art methods on benchmark datasets. However, the empirically sought parameters of the thresholding step and the circular Hough based ellipse fitting stage limit its generalization to unseen images. Also, it would not be the best of methods for cases in which the optic disc do not have a distinct edge due to disc tilting or peripapillary atrophy (PPA) and disc vessels could misguide the segmentation process. Recently deep neural network approaches have been investigated for optic disc segmentation (dos Santos Ferreira, de Carvalho Filho, de Sousa, Silva, & Gattass, 2018; Fu et al., 2018; Maninis, Pont-Tuset, Arbeláez, & Gool, 2016a) with exceptional performance on both separate-training and cross-training evaluations. Deep neural networks based approaches are highly promising for real-time screening and diagnosis due to their high accuracy and generalization on unseen data. However, this high accuracy is achieved at the cost of huge training data and high computational overhead of training. The generation of a large set of training examples involves systematic data augmentation. However, the challenge lies in generating variations that capture the underlying distribution of a high degree of varying fundus images. The high degree of inter dataset variation makes this problem highly challenging in scenarios where there is only a limited number of available

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training examples. The computational requirements of the training process of deep neural networks is yet another factor which limits their application in screening and diagnosis applications. While large clinical facilities could house high-end computing resources; not all screening facilities have such powerful computing capabilities. Also, the large training times make simultaneous training from new patients data impractical. The proposed method is favorable in scenarios where there is scarcity of training data. As the proposed method is region-based, limited number of training images could suffice to generate a reasonable number of training instances. The low training time and minimal computational requirements make the proposed method a suitable candidate for online training and updating on new patients data. Another noteworthy strength of the proposed method is that unlike previous approaches, it utilizes multiple cues and modalities to obtain a region-wise prediction. This essentially enables it to distinguish OD from other unhealthy retinal structures and provides robustness to pathology images. For offline diagnosis and annotation, where there is no time constraint, deep neural networks or ensemble methods would result in higher accuracy as compared to the proposed method. We note that the accuracy of the proposed regional classification pipeline is dependent upon the OD localization ability of the proposed approach. If the localization scheme fail on some images, the regional classification pipeline is highly likely to obtain suboptimal results. 3. Method The proposed method uses DRIONS and also further evaluated on MESSIDOR and ONHSD dataset. The main block diagram of the proposed work is depicted in Fig. 3, the details of these block are described in the following subsections. 3.1. Preprocessing In image processing, preprocessing is a preliminary step that is used to enhance the image according to the requirement for feature extraction. In this paper, this has been subdivided into three subsections. Our retinal fundus image preprocessing proposed model includes noise removal, image enhancement, and image cropping. The first step of preprocessing is to smooth the image by removing the noise and enhancing the edges of the optic disc. For this purpose, bilateral filtering is used. A bilateral filter is a nonlinear, edge-preserving and noise smoothing filter. It replaces the intensity of every pixel with a weighted normal of intensity values from adjacent pixels. This weight is founded on a Gaussian approximation. The bilateral filter is likewise characterized as a weighted normal of adjacent pixels, in a way fundamentally the same as Gaussian convolution. The difference is that the bilateral filter considers the distinction in values with the neighbors to protect edges while smoothing. The formalization of the bilateral filter is given in Eq. (1)

Np (I ) =

1  Gσ (p − n )Gσr (|Ip − In | )In , wp n∈W s

(1)

where Gσs(p − n ) is a gray level similarity function,  Gσr |Ip − In | is a geometric closeness function, Np is the filtered image and wp controls the normalization of the filtered output, given as

wp =



Gσs (p − n )Gσr (|Ip − In | ).

(2)

n∈W

The vectors p and n are the pixel locations being considered in the window W, where Ip and In are the image intensities at these pixel locations, respectively.

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Fig. 3. The train and test phases of the proposed method. The preprocessing and feature engineering blocks are common to both the train and test phases.

The next step in preprocessing is image localization and cropping around the OD region. Localization is accomplished by finding the centroid of the most intense and least eccentric region of the green channel preprocessed image. The localization process of the OD is generally invariant to the color channel of the preprocessed image. But in some cases, the performance of green channel for localizing OD is better than Red channel. Red channel usually posses the information of contrast, in other words, high contrast regions are more prominent in Red region. In case of retinal vessels image, the high contrast regions (optic disc and pathologies) are more prominent in Red channel. In images where pathologies are dominating, this channels fails to localize the true OD region as it possess pathologies having less eccentricity than OD. In contrast, in green channel, the OD of region is more eccentric than pathologies, for such cases. Eccentricity is the measure of a circular shape, higher the region shape closer to circle, lower the value of eccentricity. For this purpose, the green channel is morphologically dilated with a disk because OD is almost disk-shaped. Disk size is empirically set to be 20. A threshold computed as twice the mean intensity of the image is applied to the image to get a binary image. The thresholding criterion is adaptive which is given by

Ccrop =

M N 2  I (x, y ), M×N

(3)

x=1 y=1

where x and y are the pixel coordinates and M and N are the width and height of the image. The thresholding criterion of Eq. (3) is widely adopted in the object detection literature (Achanta, Hemami, Estrada, & Susstrunk, 2009) to segment important objects and suppress background information. This adaptive threshold is expected to only retain the high-intensity objects such as the optic disc or pathologies and suppress all the unwanted background noise. This adaptive threshold is found to be robust in our experiments as it does not have any negative effect on the eccentricity computation step. Centroid of all regions, obtained after thresholding, are calculated. Eccentricities of these regions are compared and the centroid of the one with the lowest eccentric value is used for cropping. Subimage size is experimentally set to be 200 × 200. Multiple windows sizes are placed over the localized centroid and 200 × 200 stands optimum because it neither catches extra information nor it trims the OD edges (Fig. 4). The last preprocessing step is color channel selection and histogram matching. In color image, empirically, three standard channel of color are considered mostly, i.e. (Red, Green, Blue). A color image is formed by the combination of these three channels. Most of the information for OD is in the Red channel image, therefore, this channel is used for further processing. Background information image is normalized by subtraction with its estimated

background. After that we have applied the histogram matching (Gonzalez, 2006) to normalize the image variations. 3.2. Superpixel segmentation The image is segmented into superpixel by using Simple Linear Iterative Clustering (SLIC) technique. This technique is proposed by Achanta et al. (2012) for partitioning the image into equally sized square shape. In SLIC, some parameters are required which can be selected by visualizing the variations between them and boundary observance. For initial superpixels, R is considered as superpixel region size. The center coordinates of each superpixel are iteratively updated. The grouping of pixels are based on their distance metrics of spatial and intensity values of distances. Spatial distance Ds between two consecutive pixel (e.g ath and bth)is calculated in Eq. (4)

Ds =



( xa − xb )2 + ( ya − yb )2 ,

(4)

where a and b are the pixel locality components. The intensitybased distance Di between the two adjacent pixels is defined in Eq. (5)

Di =



(Ia − Ib )2 ,

(5)

where Ia and Ib are the normalized intensity values of the ath and the bth pixel, respectively. The total distance measure, which is the combination of spatial and intensity distances, is then calculated in Eq. (6)



D=

Di + (

Ds 2 2 ) r , R

(6)

where r is a regularizer coefficient. A regularizer coefficient defines the flexibility of superpixel boundaries. If the value of r is higher, it results in more regularize segments. A lower value of r creates boundaries that are more flexible. For obtaining optimal regularizer coefficient r, the intensity values of retinal images in Eq. (6) are normalized in range [0, 1]. The normalization is performed to ensure that both of the distances (i.e. spatial and intensity) are in the same range. Fig. 5 shows cropped retinal image containing an optic disc, which is segmented into small patches of two different region sizes, R = 5 and R = 10. For both sizes, we used regularizer factor r = 0.2. 4. Feature extraction Feature extraction and normalization steps are very important to make the classification based approach more robust for OD segmentation that are detailed below.

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Fig. 4. Preprocessing. (a) Original image cropping (b) original image with Red channel (c) good contrast image for reference (d) reference image with Red channel (e) final preprocessed histogram matched image. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5. Example over-segmentations with various window sizes: (a) retinal fundus image, (b) superpixel with region size R = 5 (initial grids of 5 × 5), (c) superpixel with R = 8, (initial grids of 8 × 8) with fixed r = 0.2.

Fig. 6. Intensity based statistical features. (a) Average intensity feature, (b) maximum intensity feature, (c) minimum intensity feature, (d) median intensity feature, (e) mode intensity feature.

4.1. Intensity based statistical features Intensity based statistical features belong to the class of first order statistical features (Jain, 1989). These features directly deal with the gray level of images within the specific region (i.e. region of interest (ROI)), that are superpixels in this work. Five intensity features: average, maximum, minimum, median and mode intensity, are extracted for each superpixel. The visualization of these features is presented in Fig. 6. 4.2. Texton-map histogram Retinal images have a very complex OD structure. For this reason, we do not rely solely on intensity characteristics and therefore additionally employ texton-map based histogram features to make OD segmentation more robust. The texton-map basically capture the texture of images. The textural information is extracted from the image by convolving it with a bank of filters. The filter bank used here is created from Gabor filter. Gabor formulation is (Henriksen, 2007) defined in Eq. (7):

 G(a, b; θ , λ, ψ , γ ) = exp





−a 2 + γ 2 b 2 2σ 2



 exp i(2 × π

a



λ

 + ψ) . (7)

The σ defines the size of filter, wavelength of the sinusoidal factor is denoted by λ, phase shift is ψ , and γ for aspect ratio of spatial components. In Eq. (7), θ represents filter kernel directions that   are used to calculate the terms a and b defined in Eq. (8) and in Eq. (9): 

a = a cos θ + b sin θ , 

b = a sin θ + b cos θ .

(8) (9)

The parameter used to tune the Gabor filter bank (GFB) are discussed in more detail in the section of texton-map parameters. The retinal fundus images are convolved with the kernels of GFB. The procedure for GFB is given in Fig. 7. After convolution, response vector for all filters in the bank is generated. This response vector is then clustered into k clusters by using k-means. Features are extracted from each superpixel by the histogram value of texton-map for that superpixel. 4.3. Fractal features The fractal features are extracted by the binary decomposition of images, using a multilevel Otsu thresholding algorithm. It is important to select the optimum value of the threshold. The desired value of thresholds is discussed in parameter selection sec-

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Z.U. Rehman, S.S. Naqvi and T.M. Khan et al. / Expert Systems With Applications 120 (2019) 461–473 Table 1 Total number of extracted features. Feature name

Number of features

Statistical based features Texton histogram Fractal features Total

5 6 6 17

6. Feature selection

Fig. 7. Process of the texton map computation based on the Gabor filter bank.

tion. Otsu (Liao, Chen, Chung et al., 2001) is a multilevel thresholding method and these levels depend on the threshold value. After thresholding the image, edge detection is applied to extract the boundaries. The spatial representation for fractal features is depicted in Fig. 8. From each binary Otsu image and fractal border image, three characteristics are extracted: average intensity, area and fractal dimension, Canny (1987). The area is the sum of edge pixels in each superpixel, the mean intensity is the mean value of each superpixel in the entire threshold region. The fractal distances or Hou’s dimensions are used to find the complex binary region within each superpixel, calculated by plotting the bounding box on boundaries. The box formulation is given in Eq. (10).

0 = lim

log n( )

 →0 log  −1

,

(10)

where n( ) denotes the number of the box with side length represented by  . An approximation of fractal dimension is done by using a box counting algorithm. Fig. 9 shows the example of fractal features, extracted from the binary Otsu decomposition and canny edge image for each superpixel of the retinal fundus image. 5. Feature normalization For classification-based approaches, feature normalization (Cao, Stojkovic, & Obradovic, 2016) is important because most of the classifier work on distance based schemes. It is necessary to obtain a robust classification by normalizing the feature variable in a specific range. There are many approaches that are used to standardize the features by mean and histogram normalization. In this paper, mean normalization is used to standardize the features. Given feature f, the mathematical formulation for mean normalization is given in Eqs. (11) and (12).

Meannorm =

f − fmin , fmax − fmin

Normalization = Meannorm × (R − D ) + R.

(11) (12)

In Eq. (12) R denotes the maximum of the range of the desired target, whereas D denotes the minimum of the range of the desired target. fmax and fmin represent the maximum and minimum values of the feature f. In summary, total 17 features are extracted from each superpixel. Feature matrix includes 5 intensity statistical, 6 texton-map features and 6 fractal feature (i.e. 3 from each binary decomposed image). It is important here that the range value for normalization [0, range(texton-map)] is selected from the texton-map feature. In this paper, it is empirically selected by visualization. These are also depicted in Table 1.

Feature selection is an essential step for supervised methods. Feature selection helps in fighting with the curse of dimensionality. Good features improve the prediction performance of the classifier. In this paper, features are selected on the basis of features mutual information (MI(a, b) ) to find the minimum redundancy between feature sets (Peng, Long, & Ding, 2005). Formally, the MI is defined as follows:

MI (A, B ) =

 a∈A b∈B

ℵ(a, b). log

ℵ(a, b) , ℵ(a.b)

(13)

where ℵ(a, b) is the joint mass probability of feature a and b and ℵ(a, b) = ℵ(a.b) when a and b are statistically independent. Features are discarded based on the minimum redundancy criterion of Peng et al. (2005) until the minimum number of selected features is reached. 7. Classification In supervised classification the model is constructed by learning from data along with its annotated labels. The learned model is then evaluated on unseen data. In the proposed work, we employ four supervised classification methods for optic disc segmentation and present a comprehensive comparison on benchmark datasets. The Support Vector Machine (SVM) classifier is a supervised classifier used for classification (Boser, Guyon, & Vapnik, 1992). It can be used for both linear and nonlinear classification by using its kernels. SVM generates a hyperplane in a high-dimensional feature space, where the objective is to maximize the hyperplane margin from the support vectors. In this work, a radial basis function is adopted as the kernel, due to the nature of the problem. The random forest (RF) classifier constructs multiple decision trees based on the input variables. The RF classifier operates according to the principle of bootstrap aggregation (i.e. bagging) to increase the accuracy of generalization and reduce overfitting. Bootstrap sampling is applied to the construction of trees where a number of random predictors are utilized at each decision split. The idea is to add the responses of multiple trained trees in such a way that the correlation between trees is increased, while the variation between trees is reduced. At the test stage, the responses from multiple trees are aggregated using majority voting (Breiman, 2001). RF has become an important data analysis tool in a short period of time, and became popular because it can be applied to non-linear and higher order data sets (Strobl, Boulesteix, Zeileis, & Hothorn, 2007). The AdaBoost (Freund & Schapire, 1997) classifier aggregates the response of several weak learners to increase the generalization accuracy. The notable difference is that they are reweighted instead of sampling (Breiman, 2001). In addition, a weak learner could be compared to an RF decision tree. Random undersampling (RusBoost) is suitable for classifying imbalanced data when the instances of one class dominate many times more than the other. Machine learning techniques fail to efficiently classify skewed data, but RusBoost solved the problem by combining sampling and boosting. We explored these classification algorithms, and the results are reported in the result section.

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Fig. 8. The flow of fractal feature computation.

Fig. 9. Otsu and Canny based fractal features.

Fig. 10. The procedure of ellipse fitting. (a) Original retinal image, (b) classifier prediction overlayed on original image, (c) ellipse fitting.

8. Ellipse fitting The image segmented by the classifier is binarized for further processing. As vessels are also present in the OD region, they also affect the OD region during binarization. In addition, the boundaries of OD can be affected by the nonuniform illumination. To mitigate these effects and estimate the true OD region, the largest connected object is obtained and its boundary is used as the raw estimation. The best-fitted ellipse is computed as the OD boundary (Zhang et al., 2009). The ellipse fitting minimized the noise effects introduced by vessels especially from the inferior and superior regions of the OD neuro-retinal rim. Fig. 10 shows the effects of OD location by raw estimated boundary in Fig. 10(b) and ellipse fitted boundary in Fig. 10(c). 9. Experimental analysis In this section, the results of four nonlinear classifiers are evaluated on the publicly available retinal fundus datasets DRIONS,

ONHSD and MESSIDOR to test the robustness of the proposed method. This section reports our experiments including parameter selection and results evaluation. 9.1. Dataset description The DRIONS dataset contains 110 color retinal fundus images and annotation of optical nerve head (ONH) by two trained human experts. The data was obtained from an analogical color fundus camera and digitized using a high-resolution scanner HP-Photo Smart-S20. The size of each image is 8 bits per pixel with a resolution of 600 × 400. The average age of the patients in this database is 53.0 years with a male to female ratio is 46.2%. The ratio of patients with chronic glaucoma disease is 23.1%. The manual annotated OD segmented by the first expert has been considered as a gold standard in this paper (Fig. 11). The MESSIDOR data set contains 1200 retinal images that were collected from three different ophthalmological departments. Out of 1200 images, 800 were obtained with a dilatation of the pupil

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Fig. 11. Representatives retinal fundus images with different types of light artifacts in upper row and their ground truth manual segmentation in the lower row.

Fig. 12. Superpixel based over-segmentation with R = 8, where several variations based on different values of the regularization parameter are: (a) r = 0, (b) r = 0.2 and (c) r = 0.5.

with tropicamide at 0.5% and were captured with the 3CCD Topcon TRC camera. ONHSD database contains 99 retinal fundus images that are taken randomly from 50 patients with diabetic retinopathy; out of 99 images 96 are discernable ONH. The resolution of images is 640 × 480. 9.2. Parameter selection The determination of parameters is an essential step in the classification-based learning problems. In feature extraction, some features are nonparametric such as statistical features that are computed directly from intensity values of superpixels. The parameter for superpixel segmentation, texton map, and fractal features are imperative. In SLIC, the size and regularization parameters control the shape and size of the superpixels. They must be appropriately set such that superpixels represent the object boundaries. In the calculation of texton feature, the parameters of the Gabor filter bank and the number of K-means clusters must be determined in an appropriate manner. In our proposed method, these parameters are set during the training phase. 9.2.1. Superpixel parameters Important parameters for superpixel segmentation are the region size R and the regularizer r. The regularizer r = 0.2 is determined to be the best-suited value in our experiments, which results in superpixel shapes that represent OD boundaries, as shown in Fig. 12. The region size R is determined empirically as a function of dice score and computational complexity in this work. Table 2

Table 2 Optimal superpixel size selection for maximizing the Dice coefficient. Size of superpixel

4

6

8

10

15

Dice coefficient

0.94

0.92

0.89

0.88

0.81

Table 3 Confusion matrix.

Classify correctly Classify wrongly

OD Available

OD Not Available

True Positive (TP) False Negative (FN)

False Positive (FP) True Negative (TN)

presents the dice score for various region sizes. R = 8 is chosen in our implementation as a trade-off between accuracy in terms of Dice score and computational complexity (Figs. 14–16). 9.2.2. Texton histograms parameters For the kernel direction in Gabor filter bank, six different orientations are selected: [0°, 30°, 45°, 60, 90°, 120°]. These six directions are enough to cover the entire space with an appropriate step size. By adding more directions we can get more information, but it also adds redundant information and effect computational complexity. The wavelength coefficient and filter size are selected empirically in this work. The image with filter size 0.3 is close to the original image and appears to be quite blurred for values above 1.5. Therefore, filter sizes are selected in the range [0.3,1.5] with an increment of 0.3. The values of the wavelength coefficients that are selected by visual inspection of the filters are in the range of

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Fig. 13. Optimal tree depth selection based on minimal OoBError. The graph shows the relationship between the OoBError and the number of trees, where the optimal value for number of trees is observed to be 25, after which the error goes into steady state.

[0.8,1.5]. The number of clusters ‘k’ is set equal to the number of filter kernels, which is set to six in our implementation. 9.2.3. Fractal feature parameters In the fractal feature computation, it is important to select an appropriate threshold level. In this paper nt = 3 is chosen which creates 3 sub-levels of threshold. We selected this value owing to the considerably better Dice score for nt = 3. By increasing the levels of thresholds, the classification becomes more complex and time-consuming at fractal feature extraction. 9.3. Out of bag error (OoBError)

Five quantitative measures are employed in this work including sensitivity, specificity, Dice similarity coefficient, accuracy and area overlap. The quantitative measures are computed on the basis of the confusion matrix presented in Table 3. The quantities from the confusion matrix are explained below:

• •

TP: correctly identified OD pixels. FP: incorrectly identified non-OD pixels as OD pixels. TN: correctly identified Non-OD pixels. FN: incorrectly identified OD pixels as non-OD pixels.

Dice coefficient is a similarity measure overlap ratio between ground truth (Bernal et al., 2017) and predicted output of the classifier (Zou et al., 2004). The mathematical formulation of dice coefficient is given in Eq. (14).

Dice =

2 ∗ TP

(2 ∗ T P + F N + F P )

SN =

TP . TP + FN

(15)

Specificity (SP) is the true negative rate which means the number of negatives (healthy subjects) that are predicted as negatives. It can be measured as:

TN TN + FP

(16)

The classification accuracy is a sample of correctly identified observations in each class.

Accuracy =

TP + TN TP + FP + TN + FN

(17)

The area overlap (AOL) is defined as follows

AOL =

TP . TP + FN + FP

(18)

Ten-fold cross validation is employed for all classifiers in all of our experiments.

9.4. Evaluation measures

•

The true positive ratio is called sensitivity (SN) and also known as recall. It measures the portion of correctly identified TP as true class, given as

SP =

The prediction error of the classifier on the observations that are not used in the constructing the next base learner is known as the OoBError. OoBError is evaluated during the training stages of the learning classifiers, including RF, AdBoost and RusBoost. This empirical evaluation is sufficient to estimate the unbiased error and thus cross-validation is deemed unnecessary. Fig. 13 shows that the OoBError is minimum at 25 trees and the variation in OoBError is negligible afterwards.

•

Fig. 14. Visualization of the evaluation quantities in terms of predicted and ground truth regions. Table 3 gives better understanding of the evaluation measures.

(14)

10. Hierarchical classification In this section we present the quantitative comparison of the classifiers for the task of OD segmentation in terms of the selected measures on all datasets followed by visual results of the proposed method on representative images from benchmark databases. Next, a comparison of the proposed method with the state-of-the-art methods is presented. 10.1. Quantitative evaluation Table 5 gives a brief comparison of the regional classifiers on benchmark datasets. It can be observed that all the classifiers obtain competitive results in terms of selected performance measures. In general, the average performance of the AdaBoostM1 classifier is better than that of the SVM in terms of AOL, Dice and

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Fig. 15. Visual results of the proposed method on representative images from the DRIONS database.

Fig. 16. Visual results of the proposed approach for images from the MESSIDOR and ONHSD databases. The first row presents the visual results for SVM, RF, AdaBoostM1 and RusBoost classifiers on the MESSIDOR dataset, whereas the second row presents the results for ONHSD database.

Accuracy. Whereas the Random forest and the RusBoost classifiers obtain comparable performance notably higher than AdaBoostM1 and SVM. The major findings are summarized here:

•

•

The performance improvement of RusBoost over AdaBoostM1 can be attributed to its class imbalance handling before boosting. The Random forest classifier outperforms AdaBoostM1 on average in terms of all performance measures. This result is against the expectation as boosting generally performs better in practice than bagging. This result can be attributed to the fact that AdaBoostM1 learns from past misclassified examples, however, as OD segmentation is a class imbalanced problem, the AdaBoostM1 classifier slightly overfits to the majority class.

•

The average performance of the RusBoost classifier is comparable with the performance of the Random forest classifier. However, the low computational overhead of the Random forest classifier at the training stage makes it more feasible.

Table 4 presents a comparison of classifiers in terms of training time. Also, the individual times of the various stages of the proposed pipeline at the test stage are also reported in Table 4. It can be observed that the computation time at the test stage is dominated by the feature computation stage. The texton feature computation contributes the most to the overall feature computation time. Visual results of the proposed approach are presented in (15) and (16). The predicted boundaries by the classifiers are show

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Table 4 Timing comparison of classifiers training time and stage-wise timing of the proposed classification pipeline. Time (seconds)

Classifier training

Feature computation

Feature selection + Classification

Support vector machine Random forest AdaBoostM1 RusBoost

36.3 9.1 7.5 12.9

19.5

2.5

Table 5 Comparison of RF, SVM, AdaBoostM1 and RusBoost classifiers for optic disc classification.

DRIONS

MESSIDOR

ONHSD

AOL Dice Acc SN SP AOL Dice Acc SN SP AOL Dice Acc SN SP

Support vector machine

Random forest

AdaBoostM1

RusBoost

0.809 0.892 0.992 0.969 0.993 0.733 0.841 0.988 0.934 0.987 0.783 0.875 0.99 0.926 0.991

0.821 0.899 0.993 0.948 0.994 0.747 0.851 0.988 0.953 0.988 0.824 0.897 0.993 0.924 0.995

0.786 0.872 0.992 0.845 0.997 0.742 0.847 0.988 0.888 0.953 0.797 0.879 0.992 0.877 0.996

0.835 0.902 0.993 0.959 0.993 0.743 0.847 0.988 0.912 0.988 0.826 0.902 0.993 0.952 0.994

Table 6 Comparison of the proposed approach with the state-of-the-art methods in terms of accuracy. Author

Method+Brief detail

Database

Accuracy

1

Zahoor and Fraz (2017)

Morphology based preprocessing followed by circular Hough transform for OD localization

2

Fan et al. (2018)

3

Walter et al. (2002)

4

Morales et al. (2013)

PCA and mathematical morphology based OD segmentation

5

Abdullah et al. (2016)

Morphology based OD localization segmentation based on grow-cut algorithm

6

Proposed

DRIONS MESSIDOR DRIVE DRIONS MESSIDOR ONHSD DRIONS MESSIDOR ONHSD DRIONS MESSIDOR ONHSD DRIONS MESSIDOR ONHSD DRIONS MESSIDOR ONHSD

0.9986 0.9918 0.998 0.976 0.977 0.9895 0.9689 —– – 0.9934 0.9949 0.9941 0.9909 0.9925 1 0.993 0.998 0.997

No.

Structured learning based OD segmentation

OD extraction by watershed segmentation

Multi-parametric optic disc Segmentation using superpixel based feature classification

with different colors, while the ground truth boundary is shown in blue. 10.2. Comparison of performance measures with other algorithms The performance of the proposed method is compared with the benchmark methods in this section in terms of the five evaluation measures on three benchmark databases. The benchmark method selected for comparison include: the technique of Fan et al. (2018), Zahoor and Fraz (2017), Abdullah, Fraz, and Barman (2016), Morales et al. (2013) and Walter et al. (2002). These method are chosen due to their state-of-the-art performance and recency. It is worth mentioning here that due to the highly competitive performance of the RF classifier and its relatively lower training time, all the following comparisons are based on choosing the Random forest as the regional classifier in our proposed pipeline. Table 6 presents the comparison of the proposed method with the state-of-the-art method in terms of classification accuracy. The results show that the proposed method obtains highly competitive

results as compared with the state-of-the-art in terms of average accuracy for all datasets. Table 7 presents a comprehensive comparison of the proposed approach with the state-of-the-art methods in terms of all selected performance measures. From Table 7 it can be observed that the sensitivity of the proposed method is consistently higher than all existing methods on all datasets. Also the accuracy of the proposed method is better than the state-of-the-art methods on average for all databases. The sensitivity of the proposed method is quite competitive with the state-of-the-art on all databases with measures of 0.994, 0.988 and 0.995 on DRIONS, MESSIDOR and the ONHSD databases, respectively. The dice score of the proposed method for the DRIONS database is better than the methods of Zahoor and Fraz (2017) and Walter et al. (2002), while comparable to the rest of the benchmark methods. Similarly, the dice score of the proposed method for the ONHSD dataset is comparable to the stateof-the-art methods and slightly better than Morales et al. (2013). The overall results of the proposed method are quite promising as compared with the state-of-the-art. This improved performance of the proposed approach can be attributed to the fact that the

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Z.U. Rehman, S.S. Naqvi and T.M. Khan et al. / Expert Systems With Applications 120 (2019) 461–473 Table 7 Comparison of the proposed method with the state-of-the-art methods in terms of Sensitivity, Specificity, AOL, Dice and Accuracy measures on three benchmark datasets. Performance measures →

Sensitivity

Specificity

AOL

Dice

Accuracy

Method↓ DRIONS Zahoor and Fraz (2017) Fan et al. (2018) Walter et al. (2002) Morales et al. (2013) Abdullah et al. (2016) Proposed

0.938 0.8957 0.6715 0.928 0.8508 0.969

0.999 – – – 0.9966 0.994

0.884 0.8473 0.6227 0.842 0.851 0.821

0.889 0.9137 0.6813 0.908 0.9102 0.899

0.998 0.976 0.9689 0.993 0.9549 0.993

Messidor Zahoor and Fraz (2017) Fan et al. (2018) Morales et al. (2013) Abdullah et al. (2016) Proposed

0.889 0.9212 0.93 0.8954 0.948

0.997 – – 0.9995 0.988

0.844 0.8636 0.8228 0.879 0.747

0.903 0.9196 0.895 0.9339 0.851

0.991 0.977 0.9949 0.9989 0.988

ONHSD Zahoor and Fraz (2017) Morales et al. (2013) Abdullah et al. (2016) Proposed

0.9077 0.931 0.8857 0.924

– – 0.9992 0.995

0.8346 0.8045 0.861 0.824

0.9032 0.8867 0.9197 0.897

0.9895 0.9941 0.9967 0.993

proposed method is independent of a particular feature modality and simultaneously considers multiple OD attributes in a classification framework on a regional basis, thus improving its generalization on unseen fundus images. In contrast, majority of the morphology and the segmentation based approaches depend upon the assumption of OD being the brightest region, thus do not generalize well on pathology images. Similarly, the supervised method of Fan et al. (2018). is solely dependent upon edge information of the OD, which is an impractical assumption for peripapillary atrophy images. 11. Discussion This paper presents a supervised approach for OD localization. The presented approach used DRIONS database, ONHSD and MESSIDOR data for evaluation and relies on intensity, texton-map histogram and fractal features. In the progression of feature computation process, we considered intensity, texton-map, and fractal features. Important parameters required at the feature computation stage were sought empirically as optimization of these parameters is beyond the scope of this work. Future work will focus on incorporating more discriminative features in the proposed method for regional optic cup classification. Accurate prediction of optic cup along with optic disc is necessary to obtain reliable cup-to-disc ratio, which is an important parameter in glaucoma screening. In superpixel based OD segmentation, the superpixel size and regularization are important parameters that directly effect the accuracy and computational complexity of the proposed classification pipeline. Furthermore, the scale and shape of the superpixel can lead to inaccurate label assignment on a regional level and can cause the superpixels to miss OD boundary. Searching for an optimal region size and regularization parameters is an interesting problem, however, beyond the scope of this work. In this work, suitable values for these parameters are determined empirically. We utilized only the five extracted features that have been chosen on the basis of their mutual information with four different classifiers (SVM, AdaBoostM1, RusBoost, and RF) as in Table 5 and observed that the Random forest classifier offers the best tradeoff in terms of high accuracy and low computational complexity at the training stage. A comprehensive comparison of the proposed framework with the benchmark methods (in terms of standard performance measures on widely accepted databases) demonstrate

that its performance is comparable to the best performing benchmark methods and superior to several state-of-the-art methods. 12. Conclusion This paper presented a regional classification framework for accurate localization and segmentation of the optic disc. The modeling of the optic disc segmentation as a region-based classification by utilizing multi-modality attributes proved to be robust against the multifaceted challenges of optic disc detection. The results demonstrate that the proposed method is resilient against the highly varying nature of optic disc appearance as compared with other expert and intelligent systems, which fail due to their reliance on a single feature modality. The proposed method obtained average improvement of around 0.3% upon the performance of the best performing state-of-theart method and around 1.7% against the second best performing benchmark method in terms of accuracy. The noteworthy improvements in terms of sensitivity and accuracy obtained by the proposed method as compared with the state-of-the-art expert methods can be attributed to its independence to a particular feature of the OD. To incorporate simultaneous optic cup detection for Glaucoma screening, the proposed classification framework is to be extended to multiclass classification framework in future by incorporating useful regional features for effective discrimination of cup regions and a mutilabel classification loss formulation. Another avenue to explore in future is the class imbalance handling for both optic disc and the optic cup regions. As the feature computation stages are independent, the parallel implementation of the proposed method for obtaining real-time performance is also a subject of our future research. CRediT authorship contribution statement Zaka Ur Rehman: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing, Supervision, Project administration, Resources, Software, Software, Funding acquisition. Syed S. Naqvi: Project administration, Resources, Writing - review & editing. Tariq M. Khan: Supervision, Project administration, Resources, Writing - review & editing. Muhammad Arsalan: Visualization, Writing - review & editing. Muhammad A. Khan: Writing - review & editing. M.A. Khalil: Writing - review & editing.

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