Computer-based Cobb angle measurement using deflection points in adolescence idiopathic scoliosis from radiographic images

Areen K. Al-Bashir, Mohammad A. Al-Abed, Hala K. Amari, Fadi M. Al-Rousan, Omar M.K. Bashmaf, Enas W. Abdulhay, Rabah M. Al Abdi, N. Arunkumar, B. R.Tapas Bapu, Ahmad Khaled Al-Basheer

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Idiopathic scoliosis treatment depends on the accurate assessment of the Cobb angle, which is usually performed manually. Manual measurements, however, can lead to observer variations, which depend on the correct selection of the curvature superior and inferior vertebrae in order to draw the needed lines for Cobb angle measurements. In this paper, we are proposing an algorithm to measure the Cobb angle semi-automatically. The algorithm is based on two processing phases in which each column in the raw X-ray image is reduced to two points representing the end points of the spine and containing its general structure and outline. These points are then used to fit a fifth-order polynomial. We hypothesize that the deflection points of the fitted curve represent the superior and inferior vertebrae of the scoliosis curvature. The deflection points were used to calculate the Cobb angle. The algorithm was tested on X-ray images from 28 subjects (14 females and 14 males, average age of 15.6 ± 1.3 years) diagnosed with adolescence idiopathic scoliosis. Three manual measurements were obtained, with manually measured Cobb angles ranging from of 10° to 98°. The mean of the standard deviation of the manual readings and the algorithm results was 5.28° and 2.64°, respectively, with mean abs error of 6.6° and R value of 0.81. Excluding the cervical and rib cage touching scoliosis cases, the mean of the standard deviation of the manual readings and the algorithm results was 4.73° and 2.35°, respectively, with mean abs error of 3.78° and R value of 0.94. From the results, we can conclude that our proposed algorithm can minimize and simplify user intervention, thus allowing easier and more accurate Cobb angles measurements and resulting in a shorter diagnosis time and requiring no special skills from the user.

Original languageEnglish (US)
Pages (from-to)1547-1561
Number of pages15
JournalNeural Computing and Applications
Volume31
Issue number5
DOIs
StatePublished - May 3 2019

Fingerprint

Angle measurement
X rays
Polynomials
Processing

Keywords

  • Adolescence idiopathic scoliosis
  • Automation
  • Cobb angle
  • Deflection points
  • Digital radiography

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Computer-based Cobb angle measurement using deflection points in adolescence idiopathic scoliosis from radiographic images. / Al-Bashir, Areen K.; Al-Abed, Mohammad A.; Amari, Hala K.; Al-Rousan, Fadi M.; Bashmaf, Omar M.K.; Abdulhay, Enas W.; Al Abdi, Rabah M.; Arunkumar, N.; Bapu, B. R.Tapas; Al-Basheer, Ahmad Khaled.

In: Neural Computing and Applications, Vol. 31, No. 5, 03.05.2019, p. 1547-1561.

Research output: Contribution to journalArticle

Al-Bashir, AK, Al-Abed, MA, Amari, HK, Al-Rousan, FM, Bashmaf, OMK, Abdulhay, EW, Al Abdi, RM, Arunkumar, N, Bapu, BRT & Al-Basheer, AK 2019, 'Computer-based Cobb angle measurement using deflection points in adolescence idiopathic scoliosis from radiographic images', Neural Computing and Applications, vol. 31, no. 5, pp. 1547-1561. https://doi.org/10.1007/s00521-018-3614-y
Al-Bashir, Areen K. ; Al-Abed, Mohammad A. ; Amari, Hala K. ; Al-Rousan, Fadi M. ; Bashmaf, Omar M.K. ; Abdulhay, Enas W. ; Al Abdi, Rabah M. ; Arunkumar, N. ; Bapu, B. R.Tapas ; Al-Basheer, Ahmad Khaled. / Computer-based Cobb angle measurement using deflection points in adolescence idiopathic scoliosis from radiographic images. In: Neural Computing and Applications. 2019 ; Vol. 31, No. 5. pp. 1547-1561.
@article{f46f984543c7432c957872c18c973049,
title = "Computer-based Cobb angle measurement using deflection points in adolescence idiopathic scoliosis from radiographic images",
abstract = "Idiopathic scoliosis treatment depends on the accurate assessment of the Cobb angle, which is usually performed manually. Manual measurements, however, can lead to observer variations, which depend on the correct selection of the curvature superior and inferior vertebrae in order to draw the needed lines for Cobb angle measurements. In this paper, we are proposing an algorithm to measure the Cobb angle semi-automatically. The algorithm is based on two processing phases in which each column in the raw X-ray image is reduced to two points representing the end points of the spine and containing its general structure and outline. These points are then used to fit a fifth-order polynomial. We hypothesize that the deflection points of the fitted curve represent the superior and inferior vertebrae of the scoliosis curvature. The deflection points were used to calculate the Cobb angle. The algorithm was tested on X-ray images from 28 subjects (14 females and 14 males, average age of 15.6 ± 1.3 years) diagnosed with adolescence idiopathic scoliosis. Three manual measurements were obtained, with manually measured Cobb angles ranging from of 10° to 98°. The mean of the standard deviation of the manual readings and the algorithm results was 5.28° and 2.64°, respectively, with mean abs error of 6.6° and R value of 0.81. Excluding the cervical and rib cage touching scoliosis cases, the mean of the standard deviation of the manual readings and the algorithm results was 4.73° and 2.35°, respectively, with mean abs error of 3.78° and R value of 0.94. From the results, we can conclude that our proposed algorithm can minimize and simplify user intervention, thus allowing easier and more accurate Cobb angles measurements and resulting in a shorter diagnosis time and requiring no special skills from the user.",
keywords = "Adolescence idiopathic scoliosis, Automation, Cobb angle, Deflection points, Digital radiography",
author = "Al-Bashir, {Areen K.} and Al-Abed, {Mohammad A.} and Amari, {Hala K.} and Al-Rousan, {Fadi M.} and Bashmaf, {Omar M.K.} and Abdulhay, {Enas W.} and {Al Abdi}, {Rabah M.} and N. Arunkumar and Bapu, {B. R.Tapas} and Al-Basheer, {Ahmad Khaled}",
year = "2019",
month = "5",
day = "3",
doi = "10.1007/s00521-018-3614-y",
language = "English (US)",
volume = "31",
pages = "1547--1561",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",
number = "5",

}

TY - JOUR

T1 - Computer-based Cobb angle measurement using deflection points in adolescence idiopathic scoliosis from radiographic images

AU - Al-Bashir, Areen K.

AU - Al-Abed, Mohammad A.

AU - Amari, Hala K.

AU - Al-Rousan, Fadi M.

AU - Bashmaf, Omar M.K.

AU - Abdulhay, Enas W.

AU - Al Abdi, Rabah M.

AU - Arunkumar, N.

AU - Bapu, B. R.Tapas

AU - Al-Basheer, Ahmad Khaled

PY - 2019/5/3

Y1 - 2019/5/3

N2 - Idiopathic scoliosis treatment depends on the accurate assessment of the Cobb angle, which is usually performed manually. Manual measurements, however, can lead to observer variations, which depend on the correct selection of the curvature superior and inferior vertebrae in order to draw the needed lines for Cobb angle measurements. In this paper, we are proposing an algorithm to measure the Cobb angle semi-automatically. The algorithm is based on two processing phases in which each column in the raw X-ray image is reduced to two points representing the end points of the spine and containing its general structure and outline. These points are then used to fit a fifth-order polynomial. We hypothesize that the deflection points of the fitted curve represent the superior and inferior vertebrae of the scoliosis curvature. The deflection points were used to calculate the Cobb angle. The algorithm was tested on X-ray images from 28 subjects (14 females and 14 males, average age of 15.6 ± 1.3 years) diagnosed with adolescence idiopathic scoliosis. Three manual measurements were obtained, with manually measured Cobb angles ranging from of 10° to 98°. The mean of the standard deviation of the manual readings and the algorithm results was 5.28° and 2.64°, respectively, with mean abs error of 6.6° and R value of 0.81. Excluding the cervical and rib cage touching scoliosis cases, the mean of the standard deviation of the manual readings and the algorithm results was 4.73° and 2.35°, respectively, with mean abs error of 3.78° and R value of 0.94. From the results, we can conclude that our proposed algorithm can minimize and simplify user intervention, thus allowing easier and more accurate Cobb angles measurements and resulting in a shorter diagnosis time and requiring no special skills from the user.

AB - Idiopathic scoliosis treatment depends on the accurate assessment of the Cobb angle, which is usually performed manually. Manual measurements, however, can lead to observer variations, which depend on the correct selection of the curvature superior and inferior vertebrae in order to draw the needed lines for Cobb angle measurements. In this paper, we are proposing an algorithm to measure the Cobb angle semi-automatically. The algorithm is based on two processing phases in which each column in the raw X-ray image is reduced to two points representing the end points of the spine and containing its general structure and outline. These points are then used to fit a fifth-order polynomial. We hypothesize that the deflection points of the fitted curve represent the superior and inferior vertebrae of the scoliosis curvature. The deflection points were used to calculate the Cobb angle. The algorithm was tested on X-ray images from 28 subjects (14 females and 14 males, average age of 15.6 ± 1.3 years) diagnosed with adolescence idiopathic scoliosis. Three manual measurements were obtained, with manually measured Cobb angles ranging from of 10° to 98°. The mean of the standard deviation of the manual readings and the algorithm results was 5.28° and 2.64°, respectively, with mean abs error of 6.6° and R value of 0.81. Excluding the cervical and rib cage touching scoliosis cases, the mean of the standard deviation of the manual readings and the algorithm results was 4.73° and 2.35°, respectively, with mean abs error of 3.78° and R value of 0.94. From the results, we can conclude that our proposed algorithm can minimize and simplify user intervention, thus allowing easier and more accurate Cobb angles measurements and resulting in a shorter diagnosis time and requiring no special skills from the user.

KW - Adolescence idiopathic scoliosis

KW - Automation

KW - Cobb angle

KW - Deflection points

KW - Digital radiography

UR - http://www.scopus.com/inward/record.url?scp=85049189126&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049189126&partnerID=8YFLogxK

U2 - 10.1007/s00521-018-3614-y

DO - 10.1007/s00521-018-3614-y

M3 - Article

VL - 31

SP - 1547

EP - 1561

JO - Neural Computing and Applications

JF - Neural Computing and Applications

SN - 0941-0643

IS - 5

ER -