The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

Abstract

<p>Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%.</p>

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images

“…Currently, computer vision is widely applied in various fields, including medicine, to diagnose and analyze diseases based on medical image. The disorders diagnosed using medical images include thyroid cancer [ 1 ] on ultrasound images, back pain [ 2 ] on computed tomography scans, breast cancer [ 3 ] on mammograms, dental and oral diseases [ 4 ] on radiographic images, abnormalities of spinal intervertebral discs [ 5 , 6 ] on magnetic resonance image, and retinal diseases—diabetes mellitus [ 7 , 8 ] and glaucoma [ 9 , 10 ]—on fundus images. Glaucoma is an eye disease that may be the second-largest cause of blindness in the world.…”

Dengue classification method using support vector machines and cross-validation techniques

“…The data used in the expert system can vary, such as images [2]- [4], signals [5], or medical record data which includes name, age, laboratory test results, and symptoms of the patient [6]- [8]. In the medical field, several expert systems have been developed, for example for estimating drug doses [6], [9], monitoring the disease progress [1], [2], [10], and detecting several types of diseases such as diabetes mellitus [11], pancreatic cancer [12], breast cancer [13], glaucoma [14], and dengue fever (DF) [15]- [17]. Dengue fever is an arboviral disease caused by infection with one of the four dengue virus serotypes.…”

“…It can be applied using several methods, such as rule-based [21]- [24], or using machine learning [25]. The following methods were used in prior studies to implement the machine learning-based classification process: naive Bayes [6], logistic regression [12], random forest (RF) [8], [12], [16], k-nearest neighbor (KNN) [26], artificial neural network (ANN) [10], [13], dan support vector machine (SVM) [8], [14].…”

“…Subsequently, the extraction of the contour cup on the retinal fundus image was carried out to detect the patient of glaucoma by applying the multi-layer perceptron (MLP), KNN, naive Bayes, and SVM methods. The SVM method achieved the best accuracy results with a value of 94.44%, while the lowest results were performed by the MLP method with a value of 72.22% [14]. Improving the quality of mammogram images based on the region of interest is needed to obtain optimal breast cancer classification results using the hybrid optimum feature selection (HOFS) and ANN as the classifier feature selection method.…”