Automatic Eye Cataract Detection Algorithm Using MATLAB


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Automatic Cataract Detection using Matlab

This project proposes and evaluates an algorithm to automatically detect the cataracts from color images in adult human subjects. Currently, methods available for cataract detection are based on the use of either fundus camera or Digital Single-Lens Reflex (DSLR) camera; both are very expensive. The main motive behind this work is to develop an inexpensive, robust and convenient algorithm which in conjugation with suitable devices will be able to diagnose the presence of cataract from the true color images of an eye. An algorithm is proposed for cataract screening based on texture features: uniformity, intensity and standard deviation. These features are first computed and mapped with diagnostic opinion by the eye expert to define the basic threshold of screening system and later tested on real subjects in an eye clinic. Finally, a tele-ophthamology model using our proposed system has been suggested, which confirms the telemedicine application of the proposed system.

Demo Video


            The normal eye is made up of the sclera, cornea, pupil, aqueous humor, iris, conjunctiva, lens, vitreous humor, ciliary body, macula, retina, fovea and the optic verve. Lens is the clear part of the eye behind the iris that helps to focus light on the retina. The lens helps to focus on both far and near objects so that they are perceived clearly and sharply. The ciliary muscle helps to change the shape of the lens. This changing of the lens shape is called accommodation. It is said that the diameter of the lens is 10mm. Fig. 2(a) shows the optical image of a typical normal eye.

The research on medical images has been adopted by most of the scientists and physicians, which can assist in the detection of the abnormalities. The image processing algorithms are developed to identify the disease specific features on the medical images, which can help in the detailed study of abnormalities. The cataract is an eye disorder, which is basically detected by looking at the lens of the human eye. The anatomical changes occurred in the lens due to the abnormality is one of the decision factors for the detection of cataract. Computer processing of a medical image such as optical eye image can provide us with objective measure of the symptoms of the cataract. These objective features can be the decision factor for the abnormality detection. Pattern recognition systems can be used for the automated detection of eye abnormality using this feature vector.

            Cataract is a kind of eye disease that is a clouding in the lens of the eye that affects vision. Cataract exhibits a lot of whitish color inside a pupil. The three classes of cataracts are immature, mature and hypermature, which differ in seriousness. In an immature cataract, a whitish color appears inside the pupil but less so than in mature or hypermature cataracts. Usually, the condition is not yet serious. A Hypermature cataract exhibits much whitish color inside the pupil and can cause the lens of the eye to break if surgery is not carried out. This condition is very dangerous.

Existing Methods

            In [2], automated classification of normal, cataract and post-cataract optical eye images using SVM classifier is implemented. In this work author uses image processing techniques to detect the features in the three classes of optical eye images such as normal, cataract and post-cataract images. The features of the optical eye images such as big ring area (BRA), small ring area (SRA), edge pixel count (EPC) and object perimeter are extracted. These features are statistically analyzed and found to be significant for the automatic classification. Based on these features SVM classifier is used to classify the optical images.

In [3], an automatic approach to grade cortical and posterior sub-capsular (PSC) cataracts using retro-illumination images has been proposed. This classification is based on the region where the cataract is affected. To characterize the photometric appearances and geometric structures of cortical and PSC cataracts in retro-illumination images, low-level vision features like intensity, texture, and homogeneity is used. Then SVR classifier is used to classify the images in to cortical cataract and PSC cataract.

In [4], the author introduces a new feature for cataract grading together with a group sparsitybased constraint for linear regression, which performs feature selection, parameter selection and regression model training simultaneously. The system consists of three components: region of interest (ROI) and structure detection, feature extraction, and prediction.

In [5], a method to classify cataract lens from the non-cataract lens is introduced. The enhanced texture feature is proposed based on the graders expertise of cataract and the characteristics of the retro-illumination lens images. Here anterior image and posterior image of the eye is used to extract the features. The statistics of the enhanced texture feature is used to train the linear discriminant analysis (LDA) to detect the cataract.

In [6], a system for detection of the cataract and to test for the efficacy of the postcataract surgery using optical images is proposed using artificial intelligence techniques. Image processing and fuzzy K-means clustering algorithm is applied on the raw optical images to detect the features. Then the back propagation algorithm (BPA) is used for the classification.

All the discussed works related to computer aided cataract detection use retinal, ultrasound or slit lamp images. These systems have increased complexities, and the cost of acquisition module is very high. These devices require a trained technician for its operation. Development of a low cost system can be accomplished by using digital camera images. These devices are very common and images acquired by them need just a few instructions to be followed to be used in a system developed to detect cataracts based on these true color images. The main limitation of these discussed systems lies in determining the accurate thresholds value of multiple texture parameters such as uniformity, standard deviation and mean intensity of pupil area in digital eye image. This requires exhaustive training and validation of the developed detection algorithms using diagnostic opinion from an ophthalmologist. Further, other major limitations include complexity, ease of use and accuracy of decision. Therefore, an attempt has been made through the present work to develop a robust cataract detection algorithm based on digital color images. Here the decision regarding the presence of the cataract is made by considering three texture features, namely mean intensity, uniformity and standard deviation. Analysis of three features mapped with the diagnostic opinion of an eye expert adds robustness to the proposed algorithm.

Proposed Method:

            For the automatic detection of cataract from a digital eye image, extraction of the pupil from the image is required. The pupil of an eye has the same color in all humans, only the iris color differsfrom person to person. The pupil is circular in shape, so we can easily detect it using common circularregion detection algorithms for which Hough transform is the most common choice. It is used here toautomatically detect the circular pupil, which is distinguishable. The basic method proposed in thispaper for robust cataract detection algorithm can be described in three steps: preprocessing, featureextraction, and decision making.

Preprocessing includes conservative smoothing followed by image de-noising. The isotropic Gaussian filter is widely used as a low-pass filter for image de-noising. A two dimensional (2D) Gaussian function is simply the product of two 1D Gaussian functions.

            Gradient direction is always perpendicular to the edges. Hysteresis thresholding stage decides the real edges. After these steps, contrast enhancement is performed. These steps are followed by Histogram equalization for image enhancement. The basic idea behind this technique is mapping the intensity levels based on probability distribution of the input intensity levels.

            Feature extraction is done after preprocessing to extract all the information for cataract detection and grading from the circular pupil region. The proposed detection algorithm is based on finding the accurate thresholds of texture feature parameters such as image intensity (I), uniformity (U) standard deviation (s), to distinguish between healthy and abnormal eyes. In cataract eyes, the whitish color originates from the lens region so it can be easily concluded that cataract eyes have higher intensities than normal eyes.

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