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International Angiology 2014 December;33(6):573-89


lingua: Inglese

Automated and accurate carotid bulb detection, its verification and validation in low quality frozen frames and motion video

Ikeda N. 1, Araki T. 2, Dey N. 3, Bose S. 3, Shafique S. 4, El-Baz A. 5, Cuadrado Godia E. 6, Anzidei M. 7, Saba L. 8, Suri J. S. 3, 9, 10

1 Division of Cardiovascular Medicine, National Center for Global Health and Medicine (NCGM), Tokyo, Japan; 2 Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan; 3 Point of Care Devices, Global Biomedical Technologies, Inc., Roseville, California, USA; 4 CorVasc Vascular Laboratory, Indianapolis, IN, USA; 5 Bioengineering Department, Speed School of Engineering, Univ. of Louisville, Louisville, KY, USA; 6 IMIM. Hospital del Mar, Barcelona, Spain; 7 Department of Radiology, AOU of Cagliari, Cagliari, Italy; 8 Azienda Ospedaliero Universitaria (A.O.U.) di Cagliari, Polo di Monserrato; Università di Cagliari, Cagliari, Italy; 9 Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, USA; 10 Electrical Engineering Department (Aff.), Idaho State University ID, USA


AIM: Carotid intima-media thickness (cIMT) measurements during clinical trials need to have a fixed reference point (also called as bulb edge points) in the anatomy from which the cIMT can be measured. Identification of the bulb edge points in carotid ultrasound images faces the challenge to be detected automatically due to low image quality and variations in ultrasound images, motion artefacts, image acquisition protocols, position of the patient, and orientation of the linear probe with respect to bulb and ultrasound gain controls during acquisition.
METHODS: This paper presents a patented comprehensive methodology for carotid bulb localization and bulb edge detection as a reference point. The method consists of estimating the lumen-intima borders accurately using classification paradigm. Transition points are located automatically based on curvature characteristics. Further we verify and validate the locations of bulb edge points using combination of several local image processing methods such as (i) lumen-intima shapes, (ii) bulb slopes, (iii) bulb curvature, (iv) mean lumen thickness and its variations, and (v) geometric shape fitting.
RESULTS: Our database consists of 155 ultrasound bulb images taken from various ultrasound machines with varying resolutions and imaging conditions. Further we run our automated system blindly to spot out the bulbs in a mixture database of 336 images consisting of bulbs and no-bulbs. We are able to detect the bulbs in the bulb database with 100% accuracy having 92% as close as to a neurologists’s bulb location. Our mean lumen-intima error is 0.0133 mm with precision against the manual tracings to be 98.92%. Our bulb detection system is fast and takes on an average 9 seconds per image for detection for the bulb edge points and 4 seconds for verification/validation of the bulb edge points.

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