DOI: https://doi.org/10.14710/jtsiskom.7.2.2019.56-63

Deteksi Arteri Karotis pada Citra Ultrasound B-Mode Berbasis Convolution Neural Network Single Shot Multibox Detector

Carotid Artery Detection in B-Mode Ultrasound Images Based on Convolution Neural Network Single Shot Multibox Detector

1Department of Informatics, Universitas Pendidikan Ganesha, Indonesia

2Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia

3Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia

4 Department of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia

5 Department of Cardiology and Vascular Medicine, Universitas Indonesia, Indonesia

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Received: 25 Feb 2019; Revised: 22 Apr 2019; Accepted: 29 Apr 2019; Available online: 16 Jul 2019; Published: 30 Apr 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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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.
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Keywords: object detection; carotid artery; ultrasound B-Mode; convolutional neural network; single shot multibox detector
Funding: Kementerian Riset dan Pendidikan Tinggi;Lembaga Pengelola Dana Pendidikan (LPDP) Kementerian Keuangan

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