237

ORIGINAL
Texture analysis of sonographic muscle images can distinguishmyopathic conditions
Hiroyuki Nodera1,5, Kazuki Sogawa3, Naoko Takamatsu1, Shuji Hashiguchi2, Miho Saito2, Atsuko Mori1, 4,Yusuke Osaki1, Yuishin Izumi1, and Ryuji Kaji1
Department of Neurology, Tokushima University, Tokushima, Japan, 2Tokushima Hospital, Tokushima, Japan, 3Faculty of Medicine, Tokushima
University, Tokushima, Japan, 4Itsuki Hospital, Tokushima, Japan, 5Department of Neurology, Kanazawa Medical University, Ishikawa, Japan1

Abstract : Given the recent technological advent of muscle ultrasound (US), classification of various myopathic
conditions could be possible, especially by mathematical analysis of muscular fine structure called texture analysis. We prospectively enrolled patients with three neuromuscular conditions and their lower leg US images
were quantitatively analyzed by texture analysis and machine learning methodology in the following subjects :Inclusion body myositis (IBM) [N=11] ; myotonic dystrophy type 1 (DM1) [N=19] ; polymyositis/dermatomyositis(PM-DM) [N=21]. Although three-group analysis achieved up to 58.8% accuracy, two-group analysis of IBM plus
PM-DM versus DM1 showed 78.4% accuracy. Despite the small number of subjects, texture analysis of muscle US
followed by machine learning might be expected to be useful in identifying myopathic conditions. J. Med. Invest.
66 : 237-240, August, 2019
Keywords : myopathy, texture analysis, muscle ultrasound, machine learning

INTRODUCTION
Imaging study of skeletal muscles has been clinically utilizedin various neuromuscular conditions. Muscle ultrasound (US)
has advantages of its low cost, portability, and relative lack ofcontraindications in comparison to muscle MRI. Because muscle
US detects abnormal echo intensities (EIs) as well as abnormal
size in various myopathic conditions, characteristic localization ofabnormal EIs has been utilized as diagnostic clues in myopathies.
Muscle EIs can be assessed by a number of methods. The mostcommonly used is the mean EI in a particular region of interest(ROI). Normal muscle tissues have low EIs and surrounding
fascial tissues have high EIs. In myopathic conditions, muscle
EIs tend to increase, reflecting lipid replacement and fibrosis.
Another method is a histogram that shows the overall distribution of the tissue by observing the shape of the histogram (e.g.,skewness and kurtosis). This histogram-based characteristics
are calculated by counting the EIs from each pixel, without considering the relative relationships of adjacent pixels. By contrast,
mathematical calculation of relative relationships of adjacentpixels is called texture analysis. (1, 2) Texture analysis gives us
information about the spatial arrangement of EIs in an image.
Sogawa and colleagues reported that US-texture analysis canclassify leg muscles with neuropathic conditions and myopathic
ones. (3) However, data are lacking whether muscle echo texture
has characteristic abnormalities in different myopathic diseases.
Thus, the aim of this study was to assess whether muscle echo
texture can differentiate myopathic diseases.

METHODS
Subjects

Three groups were recruited and prospectively assessed : (1)patients with sporadic inclusion body myositis (s-IBM) who met
clinical-pathologically defined IBM or clinically defined IBM
criteria based on guidelines from the European NeuromuscularCenter (ENMC), with no family history of related conditions ; (2)patients diagnosed with either polymyositis (PM), dermatomyositis (DM), or myositis associated with a connective tissue
disease according to established diagnostic criteria ; and (3)myotonic dystrophy type 1 (DM1) who has characteristic grip
and percussion myotonia and an expansion of the cytosine-thymine-guanine (CTG) triplet repeat in the myotonic dystrophy
protein kinase gene. This study was approved by the Institutional Review Board of Tokushima University. The subjects provided
written informed consent at the time of testing.

Sonography
A single technician (N.T.) blinded to patient diagnoses performed sonography using a LOGIQ7 (GE Healthcare Japan,Tokyo) with a fixed 11-MHz linear-array transducer. The subjects were tested in the supine position and the right lower limbs
were studied. By applying the transducer to the medial aspectof the mid-calf, the medial head of gastrocnemius was identified(Figure 1). The images were saved as a bitmap format and assessed offline for the following texture analysis.

Texture analysis and machine learning
The calculation of texture features was performed by using theLIFEx program, version 3.36 (IMIV, CEA, France : https://www.

Abbreviations
DM = dermatomyositis, DM1 = myotonic dystrophy type 1, EI = echointensity, GLCM = gray level co-occurrence matrix, GLNU = graylevel non-uniformity, GLRLM = gray-level run length matrix,
GLZLM = gray-level zone length matrix, IBM = inclusion bodymyositis, NGLDM = neighborhood gray-level different matrix,
PM = polymyositis, ROI = region of interest, US = ultrasoundReceived for publication January 11, 2019 ; accepted February 19, 2019.
Address correspondence and reprint requests to Hiroyuki Nodera,MD, Department of Neurology, 3-18-15 Kuramotocho, Tokushima
City, 770-8503 Japan, and Fax : 81-88-633-7208.

The Journal of Medical Investigation Vol. 66 2019

238(A) Myotonic dystrophy type 1

H. Nodera, et al. Texture analysis of myopathy(B) Inclusion body myosisDespite the different gender distribution (male dominance inIBM and female dominance in PM/DM), the gender ratio was
not statistically significant.
Table 1: clinical characteristics of the subjects.
P valueIBM (A)

PM/DM (B)DM1 (C)

Number(men/women)

11 (7/4)

21 (6/15)

19 (9/10)

Age [mean ±SD ; (range)]75.4 ± 6.6(62 - 82)

63.1 ± 11.9
(39 - 81)

< 0.0150.4 ± 10.6
(A vs B, B vs(37 - 66)
C, C vs A)(C) Polymyosis/ Dermatomyosis0.14

Sonography

Figure 1 : Representative ultrasound images of the medial lower
leg. The regions of interest (ROIs) are superimposed where texture
calculation was performed.
lifexsoft.org/). Identical sizes of three circular-shaped regions ofinterest (ROI) were set to cover the largest area of the medial
gastrocnemius muscle, but not to include the hyperechoic epimysium. The total ROI area was set at 4.0 cm2. The LIFEx program
yields 40 imaging features : the indices from histogram ; thegray level co-occurrence (GLCM) matrix [Haralick] ; the neighborhood gray-level different (NGLDM) matrix [Amadasum] ; thegray-level run length (GLRLM) matrix [Xu] ; the gray-level zone
length (GLZLM) matrix [Thibault].
Classification was performed by WEKA machine learning
software (version 3.8.0, The University of Waikato, New Zealand). The numeric values were normalized to have a range
from 0-1. The following classifiers were used to classify theinstance : (1) Simple Logistic, (2) SMO (support vector machine),and (3) Random Forest.
Percentages of classified instances were assessed by 10-foldvalidation (90% of the instances were used as training data andthe rest of the 10% was used as the test data that are regarded as
unrelated, new data to test the established model, whose process
were repeated 10 times by selecting different portions and theaveraged percentage was provided).
In order to identify useful texture attributes for classification,
attribute selection was performed (Weka 3.8). The evaluator wasReliefFAttributeEval with the ranker search method. The top 5
attributes were selected.

Representative sonographic images are shown in Figure 1.
As previously reported, IBM and DM1 showed preferential highechodensity regions in the medial gastrocnemius. (5,6) The
representative view of LifeX texture analytic system is shownin Figure 2. Based on the calculated texture features as shownin the superimposed spreadsheet, the following analyses wereperformed. In Figure 3, comparisons of the texture features areshown among the three groups of the subjects. The mean pixelvalue (i.e., the greater the pixel value, the whiter the images)
showed no statistical significance, although IBM tended toshow greater values than the other two groups. By contrast,
the maximum pixel value showed statistical significance between DM1 and other two groups, suggesting greater pixel variability in IBM and PM/DM than DM1. NGLDM_Coarseness[NGLDM = the neighborhood grey-level different matrix] also
showed statistical significance. Figure 4 shows the comparisonof the texture features when IBM and PM/DM were grouped
together as inflammatory myopathy and compared with DM1.
Similar to Figure 3, the mean pixel values were similar
between IBM + PM/DM vs. DM1. However, the standard deviation of pixel values, histogram entropy, and the grey-level run
length matrix (GLRLM) - gray-level non-uniformity (GLNU) all
showed statistical significance.
Table 2 shows the result of classification by commonly usedmachine-learning algorithms. All three algorithms showed
greater than 76% of classification accuracies, random forest
being the top that reached 78.4% classification accuracy. In Table
3, similar classification was attempted among the three groupsof subjects. Random forest showed 58.8% of accuracy, but overall
classification was not satisfactory. Table 4 shows the selected
texture attributes that were computed as useful for classification. The most useful attribute was the standard deviation ofpixel value in the two- and three-groups analysis.

Data analysis
EZR on R commander version 1.37 was used for statisticalanalysis, using the t-test and one-way ANOVA with Holm posthoc test, where applicable. (4) A P value of 0.05 was set as thethreshold for statistical significance.

RESULTSClinical characteristics
The subject characteristics are summarized in Table 1. As
reported, the IBM patients were older than other two classes.

Figure 2 : A representative screenshot of the texture analytic software (LifeX). The calculated texture parameters are saved in a superimposed spreadsheet (the lower middle part).

239

The Journal of Medical Investigation Vol. 66 August 2019

Table 2 : Confusion matrices of classification between the two classes(inflammatory myopathy [IM] vs. myotonic dystrophy type 1 [DM1])
IM (classified)

DM1 (classified)

IM (actual)26

6

DM1 (actual)613(1) Simple Logistic (correctly classified instances : 76.5%)
IM (classified)

DM1 (classified)

IM (actual)29

3

DM1 (actual)910(2) SMO (support vector machine) (correctly classified instances : 76.5%)
IM (classified)

DM1 (classified)

IM (actual)28

4

DM1 (actual)712(3) Random Forest (correctly classified instances : 78.4%)

Figure 3 : Comparison of representative values of the three groups.
Although the mean pixel values were not statistically significant (A),other values showed significance. B = maximum pixel value ; C = thestandard deviation of pixel values ; D = NGLDM_Coarseness [NGLDM= the neighborhood grey-level different matrix]

Table 3 :Confusion matrices of classification among the three classes (inclusion body myositis [IBM], polymyositis/dermatomyositis [PM/DM], and myotonic dystrophy type 1 [DM1])
IBM (classified) PM/DM (classified) DM1 (classified)
IBM (actual)

13

1

5

PM/DM (actual)

506DM1 (actual)4314(1) Simple Logistic (correctly classified instances : 52.9%)
IBM (classified) PM/DM (classified) DM1 (classified)
IBM (actual)

150

4PM/DM (actual)

4107DM1 (actual)53

13(2) SMO (support vector machine) (correctly classified instances :54.9%)
IBM (classified) PM/DM (classified) DM1 (classified)
IBM (actual)

16

1

2PM/DM (actual)

326DM1 (actual)5412(3) Random Forest (correctly classified instances : 58.8%)

Figure 4 : Comparison of representative values of the two groups[inflammatory myopathy (IBM + PM/DM ; myotonic dystrophy type 1(DM1)]. Similar to Figure 2, the mean pixel values were similar (A), butother values showed difference. B = the standard deviation of pixel values ; C = histogram entropy ; D = GLRLM = the grey-level run length
matrix (GLRLM) - gray-level non-uniformity (GLNU)

240

H. Nodera, et al. Texture analysis of myopathy

Table 4 : Selection of texture attributes that were useful for clarification. Evaluator = ReliefFAttributeEval, search method = ranker2-class classification

3-class classification

1 : standard deviation of pixel value
2 : maximum pixel value
3 : HISTO_Entropy_log10
4 : HISTO_Entropy_log2
5 : GLRLM_GLNU

1 : standard deviation of pixel value
2 : maximum pixel value
3 : GLRLM_LRE
4 : GLRLM_RP
5 : NGLDM_Coarseness

HISTO = histogram [HISTO_Entropy reflects randomness of thedistribution] ;GLRLM = the grey-level run length matrix ;LRE = long-run emphasis [GLRLM-LRE is the distribution of thelong homogeneous runs in an image] ;RP = run percentage [GLRLM-RP measures the homogeneity of thehomogeneous runs] ;GLNU = gray-level non-uniformity [GLRLM-GLNU is the non-uniformity of the gray-levels] ;NGLDM = the neighborhood grey-level different matrix [NGLDM
corresponds to the difference of grey-level between one voxel andits 8 neighbors. NGLDM_Coarseness is the level of spatial rate ofchange in intensity]

DISCUSSION
In this study, we performed texture analysis from ultrasoundimages and machine learning methodology in patients withthree groups of myopathic conditions. Our study showed thatthe methodology classified DM1 vs. inflammatory myopathy.
Variation of pixel intensity had the highest usefulness in ourimage set.

Texture analysis
Radiomics is an emerging translational field of imaging
research aiming to extract high-dimensional data from clinicalimages. (7) Medical images are analyzed as digital information where quantitative calculation can be performed. Texture
analysis, or texture features are one of such calculated featuresthat represent coarseness or fineness of the particular portion.
Texture analysis allows objective assessment of lesion and organ
heterogeneity beyond what is possible with subjective visualinterpretation and may reflect information about the tissue microenvironment. (8)A number of imaging modalities have utilized texture analysis,including CT, MRI, PET, and ultrasound. (7) There have beensome reports utilizing texture analysis in muscle ultrasound.
Sogawa and colleagues recently showed that texture analysis
can classify neurogenic and myogenic diseases. (3) Kumbhare, etal. successfully classified the patients with myofascial pain syndrome and healthy controls by texture analysis of trapezius muscle. (9) Matta and colleagues reported that texture analysis wasa sensitive method to follow-up muscle damage. (10) Our data
were in line of the previous reports and confirmed the usefulnessof muscle texture analysis. The analysis was particularly usefulin differentiating between DM1 and inflammatory myopathy inour dataset. This information is of potential significance because
these two conditions require different therapies, particularly
immune therapy. If categories of myopathic conditions can beinferred non-invasively, patients’ need of muscle biopsy could be
reduced by texture analysis, thus muscle texture analysis can beregarded as non-invasive muscle biopsy.

Study limitations
This study has several limitations. First, the number of thesubjects are apparently small, thus widespread stages of thediseases were not covered. Second, we studied a single muscle.
Other muscles that are more severely affected in respective
myopathies might provide more useful data. Of note, however,
we have reported that gastrocnemius is one of the most severely
affected muscles in IBM and DM1, (5, 6) but obviously thigh andshoulder-girdle muscles should be more representative of abnormality in PM/DM.

CONFLICT OF INTEREST STATEMENT
None of the authors has conflict of interest.

FUNDING
This work was supported (in part) by Grants-in-Aid from theResearch on Measures for Intractable Diseases and a Healthand Labour Sciences Research Grant on Rare and Intractable
Diseases (Evidence-based Early Diagnosis and Treatment
Strategies for Neuroimmunological Diseases) from the Ministryof Health, Labour and Welfare of Japan. Also, this work wassupported by JSPS KAKENHI Grant Number 17K09800.

REFERENCES
1.

Szczypinski PM, Strzelecki M, Materka A, Klepaczko A.
MaZda--a software package for image texture analysis.
Comput Methods Programs Biomed 2009 ; 94 : 66-76.
2. Kassner A, Thornhill RE. Texture analysis : a review ofneurologic MR imaging applications. AJNR Am J Neuroradiol 2010 ; 31 : 809-816.
3. Sogawa K, Nodera H, Takamatsu N, et al. Neurogenicand Myogenic Diseases : Quantitative Texture Analysis of Muscle US Data for Differentiation. Radiology
2017 ; 283 : 492-498.
4. Kanda Y. Investigation of the freely available easy-to-use
software ‘EZR’ for medical statistics. Bone Marrow Transplant 2013 ; 48 : 452-458.
5. Nodera H, Takamatsu N, Matsui N, et al. Intramusculardissociation of echogenicity in the triceps surae characterizes sporadic inclusion body myositis. Eur J Neurol
2016 ; 23 : 588-596.
6. Takamatsu N, Sogawa K, Nodera H, et al. Preferentialchanges of skeletal muscle echogenicity in myotonic dystrophy type 1. Eur J Neurol 2017 ; 24 : 366-373.
7. Rizzo S, Botta F, Raimondi S, et al. Radiomics : the facts
and the challenges of image analysis. Eur Radiol Exp
2018 ; 2 : 36.
8. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV,Pickhardt PJ. CT Texture Analysis : Definitions, Applications, Biologic Correlates, and Challenges. Radiographics
2017 ; 37 : 1483-1503.
9. Kumbhare D, Shaw S, Ahmed S, Noseworthy MD. Quantitative ultrasound of trapezius muscle involvement in myofascial pain : comparison of clinical and healthy population
using texture analysis. J Ultrasound 2018.
10. Matta TTD, Pereira WCA, Radaelli R, Pinto RS, Oliveira
LF. Texture analysis of ultrasound images is a sensitive
method to follow-up muscle damage induced by eccentric
exercise. Clin Physiol Funct Imaging 2018 ; 38 : 477-482.