In this paper, we propose a method to extract sternocleidomastoid and longus capitis/colli automatically and
measure the thickness of those muscles from cervical vertebrae ultrasound images. Extracting sternocleidomastoid is relatively
easy but for longus capitis/colli case, due to the brightness sensitivity, it requires much more computationally burdensome
procedures. In the binarization process, instead of simple and cheap thresholding method, we apply fuzzy sigma
binarization to mitigate the sensitivity. Since that binarization procedure is computationally expensive we keep thresholding
method for sternocleidomastoid case. With considerate image processing processes such as 4-directional contour
analysis and Cubic Spline interpolation, we can successfully analyze features like muscle thickness.
In experiment, the efficacy of the proposed method is verified as having 73 ~ 87% of real world cervical vertebrae images
successfully analyzed meaning that only a small magnitude of errors in measuring thickness from medical expert's own
measurement (less than 0.1 cm for sternocleidomastoid and less than 0.3 cm for longus capitis/colli). We hope such result
encourages the use of automatic ultrasound analysis system for cervical vertebrae in rehabilitation practices.