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

I Made Gede Sunarya (Department of Informatics, Universitas Pendidikan Ganesha)
Tita Karlita (Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember)
Joko Priambodo (Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember)
Rika Rokhana (Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember)
Eko Mulyanto Yuniarno (Department of Computer Engineering, Institut Teknologi Sepuluh Nopember)
Tri Arief Sardjono (Department of Biomedical Engineering, Institut Teknologi Sepuluh Nopember)
Ismoyo Sunu (Department of Cardiology and Vascular Medicine, Universitas Indonesia)
I Ketut Eddy Purnama (Department of Computer Engineering, Institut Teknologi Sepuluh Nopember)

Jurnal Teknologi dan Sistem Komputer
Volume 7, Issue 2, Year 2019 (April 2019)

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.