EKSTRAKSI FITUR PEMBULUH DARAH CITRA FUNDUS RETINA MENGGUNAKAN FUZZY LOGIC

Ekstraksi pola pembuluh darah retina dapat dimanfaatkan dalam sistem biometrik sebagai otentikasi keamanan. Citra hasil ekstraksi pola pembuluh darah retina dapat dimasukkan ke dalam fitur untuk identifikasi sistem biometrik. Salah satu metode yang dapat dilakukan untuk melakukan segmentasi pembuluh darah retina adalah metode fuzzy logic. Pada penelitian ini, dilakukan ekstraksi pembuluh darah citra fundus retina menggunakan implementasi fuzzy logic. Peneliti menggunakan sejumlah 20 citra fundus yang diperoleh dari dataset DRIVE berformat .tif. Proses segmentasi dimulai dengan tahap preprocessing yang berisikan konversi citra menjadi grayscale, median filtering, perataan histogram CLAHE, dan eliminasi optic disc, kemudian dilanjutkan dengan pembuatan fuzzy inference system. Tahapan preprocessing yang digunakan merupakan hasil dari rangkaian uji coba peneliti dengan melihat hasil dari setiap uji coba yang dilakukan, sehingga mendapatkan citra yang menonjolkan fitur pembuluh darah dan menghilangkan noise atau fitur retina yang tidak diperlukan seperti optic disc. Uji coba segmentasi dilakukan pada Polyspace R2020a sebagai media untuk menjalankan program mulai dari preprocessing hingga segmentasi menggunakan fuzzy logic. Keluaran dari segmentasi ini berupa citra segmentasi hasil dari metode fuzzy logic dan crisp value. Metode fuzzy logic berhasil diterapkan untuk melakukan ekstraksi pembuluh darah retina dan menghasilkan crisp value. Hasil penelitian ini diharapkan dapat digunakan sebagai salah satu fitur sistem identifikasi biometrik retina

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.https://doi.org/10.3390/app802015

A Multi-Anatomical Retinal Structure Segmentation System For Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue to detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This thesis proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogenous anatomical structures

A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features

This paper proposes a new supervised method for blood vessel segmentation using Zernike moment-based shape descriptors. The method implements a pixel wise classification by computing a 11-D feature vector comprising of both statistical (gray-level) features and shape-based (Zernike moment) features. Also the feature set contains optimal coefficients of the Zernike Moments which were derived based on the maximum differentiability between the blood vessel and background pixels. A manually selected training points obtained from the training set of the DRIVE dataset, covering all possible manifestations were used for training the ANN-based binary classifier. The method was evaluated on unknown test samples of DRIVE and STARE databases and returned accuracies of 0.945 and 0.9486 respectively, outperforming other existing supervised learning methods. Further, the segmented outputs were able to cover thinner blood vessels better than previous methods, aiding in early detection of pathologies

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov