A fully automatic curve localization method for extracted spine

Aishu Xie and Ervin Gubin Moung and Xu Zhou and Zhibang Yang (2024) A fully automatic curve localization method for extracted spine. International Journal of Electrical and Computer Engineering (IJECE), 14 (4). pp. 1-16. ISSN 2722-2578

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Abstract

The automation of scoliosis positioning presents a challenging and often understated task, yet it holds fundamental significance for the automated analysis of spinal morphological anomalies. This paper introduces a novel spinal curve localization model for precisely differentiating the spinal curves and identifying their concave centers. The proposed model contains three components: i) custom spine central line model, to define the spine central line as a combination of several secant line sequences with different polarities; ii) custom curve model, to classify each spinal curve into one of 11 curves types and deduce each its concave centers by several custom formulas; and iii) adapted distance transform and quadratic line fitting algorithm coupled with custom secant line segment searching strategy (DTQL-LS), to search all line segments in the spine and group consecutive line segments with identical polarity into line sequence. Experimental results show that its positioning success rate is close to 99%. Furthermore, it exhibits significant time efficiency, with the average time to process a single image being less than 30 milliseconds. Moreover, even if some image boundaries are blurred, the center of the curve can still be accurately located.

Item Type:	Article
Keyword:	Adapted distance transform Curve type Line segment searching Line sequence Quadratic line fitting Scoliosis positioning
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) Q Science > QA Mathematics > QA1-939 Mathematics > QA440-699 Geometry. Trigonometry. Topology
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	18 Nov 2024 11:16
Last Modified:	18 Nov 2024 11:16
URI:	https://eprints.ums.edu.my/id/eprint/41932

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