Ismoyo Sunu
Faculty of Medicine, University of Indonesia/ Harapan Kita National Cardiovascular Center

Published : 2 Documents
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Endovenous laser therapy for varicose vein Mulia, Erwin; Dakota, Iwan; Andriantoro, Hananto; Kaligis, R. W.M.; Sunu, Ismoyo
Medical Journal of Indonesia Vol 22, No 2 (2013): May
Publisher : Faculty of Medicine Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (565.521 KB) | DOI: 10.13181/mji.v22i2.539

Abstract

Laser has become a useful technology in treating venous incompetence especially superficial venous disease. Introduction of endovenous thermal ablation through endovenous laser therapy helped by duplex ultrasound guidance has provided an alternative for traditional saphenous vein stripping. High success rate, minor complications, and minimally invasive technique provide the advantages over traditional treatment. In this case illustrated, the endovenous laser therapy used for great saphenous varicose vein. Yet, future development in endovenous laser therapy is still needed and only long term follow-up and uniform reporting standards will provide the answers. (Med J Indones. 2013;22:117-20)Keywords: Endovenous laser theraphy, great saphenous vein, varicose vein
Carotid Artery Detection in B-Mode Ultrasound Images Based on Convolution Neural Network Single Shot Multibox Detector Sunarya, I Made Gede; Karlita, Tita; Priambodo, Joko; Rokhana, Rika; Yuniarno, Eko Mulyanto; Sardjono, Tri Arief; Sunu, Ismoyo; Purnama, I Ketut Eddy
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 2, Year 2019 (April 2019)
Publisher : Departemen Teknik Sistem Komputer, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1288.438 KB) | DOI: 10.14710/jtsiskom.7.2.2019.56-63

Abstract

Detection of vascular areas (blood vessels) using B-Mode ultrasound images is needed for automatic applications such as registration and navigation in medical operations. This study developed the detection of the carotid artery area using Convolution Neural Network Single Shot Network Multibox Detector (SSD) to determine the bounding box ROI of the carotid artery area in B-mode ultrasound images. The data used are B-Mode ultrasound images on the neck that contain the carotid artery area (primary data). SSD method result is 95% of accuracy which is higher than the Hough transformation method, Ellipse method, and Faster RCNN in detecting carotid artery area in the B-Mode ultrasound image. The use of image enhancement with Gaussian filter, histogram equalization, and Median filters in this method can increase detection accuracy. The best process time of the proposed method is 2.09 seconds so that it can be applied in a real-time system.