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RISS 인기검색어
- 검색키워드
- 저자 : Anindita Septiarini (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation
Anindita Septiarini,Agus Harjoko,Reza Pulungan,Retno Ekantini 대한의료정보학회 2018 Healthcare Informatics Research Vol.24 No.4
Objectives: The retinal nerve fiber layer (RNFL) is a site of glaucomatous optic neuropathy whose early changes need to be detected because glaucoma is one of the most common causes of blindness. This paper proposes an automated RNFL detection method based on the texture feature by forming a co-occurrence matrix and a backpropagation neural network as the classifier. Methods: We propose two texture features, namely, correlation and autocorrelation based on a co-occurrence matrix. Those features are selected by using a correlation feature selection method. Then the backpropagation neural network is applied as the classifier to implement RNFL detection in a retinal fundus image. Results: We used 40 retinal fundus images as testing data and 160 sub-images (80 showing a normal RNFL and 80 showing RNFL loss) as training data to evaluate the performance of our proposed method. Overall, this work achieved an accuracy of 94.52%. Conclusions: Our results demonstrated that the proposed method achieved a high accuracy, which indicates good performance.
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Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images
Anindita Septiarini,Dyna M. Khairina,Awang H. Kridalaksana,Hamdani Hamdani 대한의료정보학회 2018 Healthcare Informatics Research Vol.24 No.1
Objectives: Glaucoma is an incurable eye disease and the second leading cause of blindness in the world. Until 2020, thenumber of patients of this disease is estimated to increase. This paper proposes a glaucoma detection method using statisticalfeatures and the k-nearest neighbor algorithm as the classifier. Methods: We propose three statistical features, namely,the mean, smoothness and 3rd moment, which are extracted from images of the optic nerve head. These three features areobtained through feature extraction followed by feature selection using the correlation feature selection method. To classifythose features, we apply the k-nearest neighbor algorithm as a classifier to perform glaucoma detection on fundus images. Results: To evaluate the performance of the proposed method, 84 fundus images were used as experimental data consistingof 41 glaucoma image and 43 normal images. The performance of our proposed method was measured in terms of accuracy,and the overall result achieved in this work was 95.24%, respectively. Conclusions: This research showed that the proposedmethod using three statistics features achieves good performance for glaucoma detection
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Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images
Anindita Septiarini,Hamdani Hamdani,Emy Setyaningsih,Eko Junirianto,Fitri Utaminingrum 대한의료정보학회 2023 Healthcare Informatics Research Vol.29 No.2
Objectives: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learningbased on a convolutional neural network (CNN). Methods: This study used private and public datasets containing retinalfundus images. The private dataset consisted of 350 images, while the public dataset was the Retinal Fundus Glaucoma Challenge(REFUGE). The proposed method was based on a CNN with a single-shot multibox detector (MobileNetV2) to formimages of the region-of-interest (ROI) using the original image resized into 640 × 640 input data. A pre-processing sequencewas then implemented, including augmentation, resizing, and normalization. Furthermore, a U-Net model was applied foroptic disc segmentation with 128 × 128 input data. Results: The proposed method was appropriately applied to the datasetsused, as shown by the values of the F1-score, dice score, and intersection over union of 0.9880, 0.9852, and 0.9763 for the privatedataset, respectively, and 0.9854, 0.9838 and 0.9712 for the REFUGE dataset. Conclusions: The optic disc area producedby the proposed method was similar to that identified by an ophthalmologist. Therefore, this method can be considered forimplementing automatic segmentation of the optic disc area.
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Fitri Utaminingrum,Sri Mayena,I Komang Somawirata,Anindita Septiarini,Timothy K. Shih 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.1
Many electric powered wheelchairs (EPW) users fall due to the user’s carelessness of the road conditions in front of them that will have a significant impact on accidents. The process for detecting road conditions is one solution to maintain the safety of EPW users. This research is conducted to develop autonomous systems in the wheelchair to detect stair descent and floor obstacles. The system accomplished to prevent fatal risks occurs to the user, such as falling from the stairs that cause fractures. Moreover, the main goal of the system expansion is to identify the best kernel class from the support vector machine (SVM) classification method to distinguish the stair descent and the floor. This experiment is completed using the SVM method classified into four kernel functions: linear, polynomial, Gaussian, and Sigmoid kernel class, and also associated with gray-level co-occurrence matrix (GLCM) features extraction. The SVM produces the best result for detecting used linear kernel function with GLCM parameters (d = 1, θ = 0) was reached an average of accuracy is 89.0% for image data testing and video testing is 82.6%.
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