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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

General Research Article

Automatic Characterizations of Lumbar Multifidus Muscle and Intramuscular Fat with Fuzzy C-means based Quantization from Ultrasound Images

Author(s): Kwang Baek Kim*, Hyun Jun Park and Doo Heon Song

Volume 16, Issue 5, 2020

Page: [592 - 600] Pages: 9

DOI: 10.2174/1573405615666181224141358

Abstract

Background: Low Back Pain (LBP) is a common disorder involving the muscles and bones and about half of the people experience LBP at some point of their lives. Since the social economic cost and the recurrence rate over the lifetime is very high, the treatment/rehabilitation of chronic LBP is important to physiotherapists, both for clinical and research purposes. Trunk muscles such as the lumbar multifidi is important in spinal functions and intramuscular fat is also important in understanding pain control and rehabilitations. However, the analysis of such muscles and related fat require many human interventions and thus suffers from the operator subjectivity especially when the ultrasonography is used due to its cost-effectiveness and no radioactive risk.

Aims: In this paper, we propose a fully automatic computer vision based software to compute the thickness of the lumbar multifidi muscles and to analyze intramuscular fat distribution in that area.

Methods: The proposed system applies various image processing algorithms to enhance the intensity contrast of the image and measure the thickness of the target muscle. Intermuscular fat analysis is done by Fuzzy C-Means (FCM) clustering based quantization.

Results: In experiment using 50 DICOM format ultrasound images from 50 subjects, the proposed system shows very promising result in computing the thickness of lumbar multifidi.

Conclusion: The proposed system have minimal discrepancy(less than 0.2 cm) from human expert for 72% (36 out of 50 cases) of the given data. Also, FCM based intramuscular fat analysis looks better than conventional histogram analysis.

Keywords: Lumbar multifidus muscle, fuzzy C-means, intramuscular fat, ultrasonography, low back pain, radioactive risk.

Graphical Abstract
[1]
Rundell SD, Sherman KJ, Heagerty PJ, Mock CN, Jarvik JG. The clinical course of pain and function in older adults with a new primary care visit for back pain. J Am Geriatr Soc 2015; 63(3): 524-30.
[http://dx.doi.org/10.1111/jgs.13241] [PMID: 25754841]
[2]
Katz JN. Lumbar disc disorders and low-back pain: socioeconomic factors and consequences. J Bone Joint Surg Am 2006; 88(Suppl. 2): 21-4.
[http://dx.doi.org/10.2106/00004623-200604002-00005] [PMID: 16595438]
[3]
Mehra M, Hill K, Nicholl D, Schadrack J. The burden of chronic low back pain with and without a neuropathic component: a healthcare resource use and cost analysis. J Med Econ 2012; 15(2): 245-52.
[http://dx.doi.org/10.3111/13696998.2011.642090] [PMID: 22136441]
[4]
Sions JM, Coyle PC, Velasco TO, Elliott JM, Hicks GE. Multifidi muscle characteristics and physical function among older adults with and without chronic low back pain. Arch Phys Med Rehabil 2017; 98(1): 51-7.
[http://dx.doi.org/10.1016/j.apmr.2016.07.027] [PMID: 27590444]
[5]
Dagenais S, Caro J, Haldeman S. A systematic review of low back pain cost of illness studies in the United States and internationally. Spine J 2008; 8(1): 8-20.
[http://dx.doi.org/10.1016/j.spinee.2007.10.005] [PMID: 18164449]
[6]
Manek NJ, MacGregor AJ. Epidemiology of back disorders: prevalence, risk factors, and prognosis. Curr Opin Rheumatol 2005; 17(2): 134-40.
[PMID: 15711224]
[7]
Freeman MD, Woodham MA, Woodham AW. The role of the lumbar multifidus in chronic low back pain: a review. PM R 2010; 2(2): 142-6.
[http://dx.doi.org/10.1016/j.pmrj.2009.11.006] [PMID: 20193941]
[8]
Luoto S, Aalto H, Taimela S, Hurri H, Pyykkö I, Alaranta H. One-footed and externally disturbed two-footed postural control in patients with chronic low back pain and healthy control subjects. A controlled study with follow-up. Spine 1998; 23(19): 2081-9.
[http://dx.doi.org/10.1097/00007632-199810010-00008] [PMID: 9794052]
[9]
Kim SY, Baek IH. Effects of transversus abdominal muscle stabilization exercise to spinal segment motion on trunk flexion-extension. Physic Ther Korea 2003; 10(1): 63-76.
[10]
Hebert JJ, Koppenhaver SL, Parent EC, Fritz JM. A systematic review of the reliability of rehabilitative ultrasound imaging for the quantitative assessment of the abdominal and lumbar trunk muscles. Spine 2009; 34(23): E848-56.
[http://dx.doi.org/10.1097/BRS.0b013e3181ae625c] [PMID: 19927091]
[11]
Wilson A, Hides JA, Blizzard L, et al. Measuring ultrasound images of abdominal and lumbar multifidus muscles in older adults: A reliability study. Man Ther 2016; 23: 114-9.
[http://dx.doi.org/10.1016/j.math.2016.01.004] [PMID: 26832788]
[12]
Wong AY, Parent EC, Funabashi M, Kawchuk GN. Do changes in transversus abdominis and lumbar multifidus during conservative treatment explain changes in clinical outcomes related to nonspecific low back pain? A systematic review. J Pain 2014; 15(4): 377.e1-377.e35.
[http://dx.doi.org/10.1016/j.jpain.2013.10.008] [PMID: 24184573]
[13]
Mengiardi B, Schmid MR, Boos N, et al. Fat content of lumbar paraspinal muscles in patients with chronic low back pain and in asymptomatic volunteers: quantification with MR spectroscopy. Radiology 2006; 240(3): 786-92.
[http://dx.doi.org/10.1148/radiol.2403050820] [PMID: 16926328]
[14]
Hebert JJ, Kjaer P, Fritz JM, Walker BF. The relationship of lumbar multifidus muscle morphology to previous, current, and future low back pain: a 9-year population-based prospective cohort study. Spine 2014; 39(17): 1417-25.
[http://dx.doi.org/10.1097/BRS.0000000000000424] [PMID: 24859576]
[15]
Hicks GE, Simonsick EM, Harris TB, et al. Trunk muscle composition as a predictor of reduced functional capacity in the health, aging and body composition study: the moderating role of back pain. J Gerontol A Biol Sci Med Sci 2005; 60(11): 1420-4.
[http://dx.doi.org/10.1093/gerona/60.11.1420] [PMID: 16339328]
[16]
Storheim K, Berg L, Hellum C, et al. Norwegian Spine Study Group. Fat in the lumbar multifidus muscles - predictive value and change following disc prosthesis surgery and multidisciplinary rehabilitation in patients with chronic low back pain and degenerative disc: 2-year follow-up of a randomized trial. BMC Musculoskelet Disord 2017; 18(1): 145.
[http://dx.doi.org/10.1186/s12891-017-1505-5] [PMID: 28376754]
[17]
Kim Y, Lee J, Park S, et al. Effects of lumbar stability exercise on the muscle thickness and contraction time using sound wave vibrator and Swiss Ball. J Korean Soc Integr Med 2016; 4(1): 85-97.
[http://dx.doi.org/10.15268/ksim.2016.4.1.085]
[18]
Kim TH, Hahn J, Jeong JR, et al. Changes of abdominal muscle thickness during stable and unstable surface bridging exercise in young people. Phys Ther Rehab Sci 2016; 5(4): 210-4.
[http://dx.doi.org/10.14474/ptrs.2016.5.4.210]
[19]
Goldby LJ, Moore AP, Doust J, Trew ME. A randomized controlled trial investigating the efficiency of musculoskeletal physiotherapy on chronic low back disorder. Spine 2006; 31(10): 1083-93.
[http://dx.doi.org/10.1097/01.brs.0000216464.37504.64] [PMID: 16648741]
[20]
van Middelkoop M, Rubinstein SM, Kuijpers T, et al. A systematic review on the effectiveness of physical and rehabilitation interventions for chronic non-specific low back pain. Eur Spine J 2011; 20(1): 19-39.
[http://dx.doi.org/10.1007/s00586-010-1518-3] [PMID: 20640863]
[21]
Teyhen DS, Gill NW, Whittaker JL, Henry SM, Hides JA, Hodges P. Rehabilitative ultrasound imaging of the abdominal muscles. J Orthop Sport Phys 2007; 37(8): 450-66.
[22]
Teyhen DS, Bluemle LN, Dolbeer JA, et al. Changes in lateral abdominal muscle thickness during the abdominal drawing-in maneuver in those with lumbopelvic pain. J Orthop Sport Phys 2009; 39(11): 791-8.
[23]
Hodges PW, Moseley GL. Pain and motor control of the lumbopelvic region: effect and possible mechanisms. J Electromyogr Kinesiol 2003; 13(4): 361-70.
[http://dx.doi.org/10.1016/S1050-6411(03)00042-7] [PMID: 12832166]
[24]
Whittaker JL, Teyhen DS, Elliott JM, et al. Rehabilitative ultrasound imaging: understanding the technology and its applications. J Orthop Sport Phys 2007; 37(8): 434-9.
[25]
Park J, Song DH, Han SS, Lee SJ, Kim KB. Automatic extraction of soft tissue tumor from ultrasonography using ART2 based intelligent image analysis. Curr Med Imaging 2017; 13(4): 447-53.
[http://dx.doi.org/10.2174/1573405613666170504153002]
[26]
Gupta R, Elamvazuthi I, Dass SC, et al. Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method. Biomed Eng Online 2014; 13(1): 157.
[http://dx.doi.org/10.1186/1475-925X-13-157] [PMID: 25471386]
[27]
Suryadibrata A, Song DH, Kim KB. Automatic ganglion cyst detection from ultrasound images using fuzzy c-means clustering method. Inte Inform Inste (Tokyo). Inform 2017; 120(4A): 2543-8.
[28]
Lee HJ, Song DH, Kim KB. Effective computer-assisted automatic cervical vertebrae extraction with rehabilitative ultrasound imaging by using K-means clustering. Int J Electr Comput Eng 2016; 6(6): 2810-7.
[29]
Kim KB, Park HJ, Song DH, Han SS. Extraction of sternocleidomastoid and longus capitis/colli muscle using cervical vertebrae ultrasound images. Curr Med Imaging 2014; 10(2): 95-104.
[http://dx.doi.org/10.2174/157340561002140715101740]
[30]
Kutbay U, Hardalaç F, Akbulut M, Akaslan Ü, Serhatlıoğlu S. A Computer-Aided Diagnosis system for measuring carotid artery Intima-Media Thickness (IMT) using quaternion vectors. J Med Syst 2016; 40(6): 149.
[http://dx.doi.org/10.1007/s10916-016-0507-4] [PMID: 27137786]
[31]
Xian M, Zhang Y, Cheng HD, Xu F, Zhang B, Ding J. Automatic breast ultrasound image segmentation: a survey. Pattern Recognit 2018; 79: 340-55.
[32]
Kim KB. A fully automatic measurement of lumbar multifidus muscle thickness from ultrasound image. J Med Imaging Health Inform 2015; 5(1): 1-6.
[http://dx.doi.org/10.1166/jmihi.2015.1357]
[33]
Lui D, Scharfenberger C, De Carvalho DE, Callaghan JP, Wong A. Semi-automatic Fisher-Tippett guided active contour for lumbar multifidus muscle segmentation. Conf Proc IEEE Eng Med Biol Soc 2014; 2014: 5530-3.
[http://dx.doi.org/10.1109/EMBC.2014.6944879]
[34]
Arai K, Eguchi Y, Kitajima Y. Extraction of line features from multifidus muscle of ct scanned images with morphologic filter together with wavelet multi resolution analysis. IJACSA 2011; 1(3): 60-8.
[35]
Kim KB, Song DH, Lee WJ. Flaw detection in ceramics using sigma fuzzy binarization and gaussian filtering method. IJMUE 2014; 9(1): 403-14.
[http://dx.doi.org/10.14257/ijmue.2014.9.1.37]
[36]
Kanth AR, Reddy YN. Cubic spline for a class of singular two-point boundary value problems. Appl Math Comput 2005; 170(2): 733-40.
[http://dx.doi.org/10.1016/j.amc.2004.12.049]
[37]
Kim KB, Song DH. Defect detection method using fuzzy stretching and ART2 learning from ceramic images. Int J Softw Eng Appl 2014; 8(9): 29-38.
[38]
Park SI, Park HJ, Kim KB. Appendix analysis from ultrasonography with cubic spline interpolation and K-means clustering. Int. J Bio-Sci and Bio-Tech 2015; 7(1): 1-10.
[http://dx.doi.org/10.14257/ijbsbt.2015.7.1.01]
[39]
Kim KB, Lee HJ, Song DH, Woo YW. Extracting fascia and analysis of muscles from ultrasound images with FCM-based quantization technology. Neural Netw World 2010; 20(3): 405-16.
[40]
Gonzalez RC, Woods RE. Digital image processing. Prentice Hall 2002.
[41]
Sutherlin MA, Gage M, Mangum LC, et al. Changes in muscle thickness across positions on ultrasound imaging in participants with or without a history of low back pain. J Athl Train 2018; 53(6): 553-9.
[http://dx.doi.org/10.4085/1062-6050-491-16] [PMID: 29912568]

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