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All Journal Jurnal Teknologi dan Sistem Komputer
Tita Karlita
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember
Computer Science & IT Electrical & Electronics Engineering

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Deteksi Arteri Karotis pada Citra Ultrasound B-Mode Berbasis Convolution Neural Network Single Shot Multibox Detector I Made Gede Sunarya; Tita Karlita; Joko Priambodo; Rika Rokhana; Eko Mulyanto Yuniarno; Tri Arief Sardjono; Ismoyo Sunu; I Ketut Eddy Purnama
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 2, Year 2019 (April 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, 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.
Co-Authors Eko Mulyanto Yuniarno I Ketut Eddy Purnama I Made Gede Sunarya Ismoyo Sunu Joko Priambodo Rika Rokhana Tri Arief Sardjono