Open AccessDeteksi Arteri Karotis pada Citra Ultrasound B-Mode Berbasis Convolution Neural Network Single Shot Multibox Detector
Author(s) -
I Made Gede Sunarya,
Tita Karlita,
Joko Priambodo,
Rika Rokhana,
Eko Mulyanto Yuniarno,
Tri Arief Sardjono,
Ismoyo Sunu,
I Ketut Eddy Purnama
Publication year - 2019
Publication title -
jurnal teknologi dan sistem komputer
Language(s) - English
Resource type - Journals
eISSN - 2620-4002
pISSN - 2338-0403
DOI - 10.14710/jtsiskom.7.2.2019.56-63
Subject(s) - artificial intelligence , computer vision , ultrasound , hough transform , detector , convolutional neural network , computer science , median filter , medicine , radiology , image processing , image (mathematics) , telecommunications
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.