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Perbandingan Metode High-Frequency Emphasis (HFE) Dan Contrast Limited Adaptive Histogram Equalization (CLAHE) Dalam Perbaikan Kualitas Citra Penginderaan Jauh (Remote Sensing) Andi Irvan Zakaria; Ernawati Ernawati; Arie Vatresia; Widhia KZ Oktoeberza
Jurnal Pseudocode Vol 6, No 2 (2019): Volume 6 Nomor 2 September 2019
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1348.766 KB) | DOI: 10.33369/pseudocode.6.2.125-137

Abstract

Citra penginderaan jauh merupakan objek hasil perekaman sensor ataupun suatu aplikasi pemantau penginderaan jauh. Namun terkadang citra yang dihasilkan memiliki kualitas yang rendah. Metode High-Frequency Emphasis (HFE) dan Contrast Limited Adaptive Histogram Equalization (CLAHE) merupakan algoritme yang baik dalam memperbaiki kualitas citra penginderaan jauh (remote sensing). Metode HFE  bersifat mempertahankan frekuensi tinggi dan menekan frekuensi rendah. Pada penelitian ini, metode CLAHE mampu meningkatkan 8 citra dari 20 citra SAS-planet yang diujikan. Sedangkan pada metode HFE hanya 4 citra yang memiliki PSNR di atas 30 dB. Hasil penelitian ini  mengindikasikan bahwa performa CLAHE lebih baik dibanding metode HFE dalam meningkatkan kulitas citra penginderaan jauh. Kata kunci: Citra Penginderaan Jauh, HFE, CLAHE
Dark lesion elimination based on area, eccentricity and extent features for supporting haemorrhages detection Vesi Yulyanti; Hanung Adi Nugroho; Igi Ardiyanto; Widhia KZ Oktoeberza
Communications in Science and Technology Vol 4 No 1 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (410.365 KB) | DOI: 10.21924/cst.4.1.2019.110

Abstract

One of the complications due to the long-term of diabetes is retinal vessels damaging called diabetic retinopathy. It is characterised by appearing the bleeding spots in the large size (haemorrhages) on the surface of retina. Early detection of haemorrhages is needed for preventing the worst effect which leads to vision loss. This study aims to detect haemorrhages by eliminating other dark lesion objects that have similar characteristics with haemorrhages based on three features, i.e. area, eccentricity and extent features. This study uses 43 retinal fundus images taken from DIARETDB1 database. Based on the validation process, the average level of sensitivity gained is 80.5%. These results indicate that the proposed method is quite capable of detecting haemorrhages which appear in the retinal surface.