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

*I Made Gede Sunarya -  Department of Informatics, Universitas Pendidikan Ganesha, Indonesia
Tita Karlita -  Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Joko Priambodo -  Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Rika Rokhana -  Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Eko Mulyanto Yuniarno -  Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Tri Arief Sardjono -  Department of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Ismoyo Sunu -  Department of Cardiology and Vascular Medicine, Universitas Indonesia, Indonesia
I Ketut Eddy Purnama -  Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Received: 25 Feb 2019; Revised: 22 Apr 2019; Accepted: 29 Apr 2019; Published: 30 Apr 2019; Available online: 16 Jul 2019.
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Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Article Info
Section: Articles
Language: ID
In Volume 7, Issue 2, Year 2019 (April 2019)
Statistics: 186 157
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.
Keywords
object detection; carotid artery; ultrasound B-Mode; convolutional neural network; single shot multibox detector

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