# Quantitative Analysis of Patellar Tendon Abnormality in Asymptomatic Professional “Pallapugno” Players: A Texture-Based Ultrasound Approach

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## Abstract

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## Featured Application

**Quantitative texture analysis of tendon ultrasound images for determination of subclinical tendinopathy.**

## Abstract

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Subject Database

#### 2.2. Ultrasound Image Acquisition and Protocol

#### 2.3. Texture Feature Extraction

#### 2.3.1. First-Order Statistical Descriptors

#### 2.3.2. Haralick Features

#### 2.3.3. Higher-Order Spectra, Entropy Features, and Hu’s Moments

#### 2.4. Statistical Analysis

## 3. Results

#### 3.1. Comparison between Dominant and Non-Dominant Side

#### 3.2. Comparison between Subclinical Tendinopathy and Non-Subclinical Tendinopathy

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Representative patellar tendon images of one player displaying both the dominant and non-dominant side.

**Figure 2.**Representative patellar tendon images showing the non-dominant side of two players, one with a normal tendon structure (

**right panels**), and one player with an abnormal tendon structure (

**left panels**). An abnormal tendon structure was taken as one that fell into the category of subclinical tendinopathy as arbitrarily defined in the study by Giacchino et al. [23]. All scans reported in the figure were acquired with the knee at a 30° position. Long: longitudinal.

**Figure 3.**Results obtained when comparing dominant and non-dominant side texture features considering all subjects (

**first row**) and only subjects with normal tendon structure (

**second row**). The black circles represent the dominant side textures features, while the gray circles represent the non-dominant side texture features. (

**a**) Representation of all subjects in the plane of the first two canonical variables obtained by a MANOVA analysis. The vertical gray dotted line represents the optimal threshold used (maximum Youden index) to obtain the sensitivity (86.9%), specificity (79.8%), and accuracy (83.3%) results; (

**b**) ROC analysis results demonstrating the classification quality of the first MANOVA canonical variable, with an AUC equal to 0.906; (

**c**) Representation of the healthy subjects in the plane of the first two canonical variables obtained by a MANOVA analysis. The vertical gray dotted line represents the optimal threshold used (maximum Youden index) to obtain the sensitivity (96.3%), specificity (96.3%), and accuracy (96.3%) results; (

**d**) ROC analysis results demonstrating the classification quality of the first MANOVA canonical variable, with an AUC equal to 0.989.

**Figure 4.**Results obtained when comparing subjects with (n = 5) and without subclinical tendinopathy (n = 9). (

**a**) Representation of the subjects in the plane of the first two canonical variables obtained by a MANOVA analysis. The black circles represent the texture features of subjects with subclinical tendinopathy, while the gray circles represent the texture features of subjects without subclinical tendinopathy. The vertical gray dotted line represents the optimal threshold (maximum Youden index) used to obtain the sensitivity (93.3%), specificity (90.7%), and accuracy (91.7%) results; (

**b**) ROC analysis results demonstrating the classification quality of the first MANOVA canonical variable, with an AUC equal to 0.967.

Plane | Probe Position | Knee Angle |
---|---|---|

Transversal | Proximal | 0° |

Longitudinal | Proximal | 0° |

Transversal | Proximal | 30° |

Longitudinal | Proximal | 30° |

Transversal | Central | 30° |

Longitudinal | Central | 30° |

Feature Name | Mathematical Description |
---|---|

Mean (m) | $m={\displaystyle \sum}_{x=1}^{M}{\displaystyle \sum}_{y=1}^{N}\frac{I\left(x,y\right)}{M\times N}$ |

Standard deviation ($\sigma $) | $\sigma =\sqrt{\frac{{{\displaystyle \sum}}_{x=1}^{M}{{\displaystyle \sum}}_{y=1}^{N}{\left\{I\left(x,y\right)-m\right\}}^{2}}{M\times N}}$ |

Variance (${\sigma}^{2}$) | ${\sigma}^{2}=\frac{{{\displaystyle \sum}}_{x=1}^{M}{{\displaystyle \sum}}_{y=1}^{N}{\left\{I\left(x,y\right)-m\right\}}^{2}}{M\times N}$ |

Skewness (S_{k}) | ${S}_{k}=\frac{1}{M\times N}\frac{{{\displaystyle \sum}}_{x=1}^{M}{{\displaystyle \sum}}_{y=1}^{N}{\left\{I\left(x,y\right)-m\right\}}^{3}}{{\sigma}^{3}}$ |

Kurtosis (K_{t}) | ${K}_{t}=\frac{1}{M\times N}\frac{{{\displaystyle \sum}}_{x=1}^{M}{{\displaystyle \sum}}_{y=1}^{N}{\left\{I\left(x,y\right)-m\right\}}^{4}}{{\sigma}^{4}}$ |

Energy_{1} (E_{1}) | ${E}_{1}={\displaystyle \sum}_{x=1}^{M}{\displaystyle \sum}_{y=1}^{N}I{\left(x,y\right)}^{2}$ |

Feature Name | Mathematical Description |
---|---|

Symmetry (I_{sym}) | ${I}_{sym}=1-{\displaystyle \sum}_{i=0}^{N-1}{\displaystyle \sum}_{j=0}^{N-1}\left|i-j\right|P\left(i,j\right)$ |

Contrast (I_{con}) | ${I}_{con}={\displaystyle \sum}_{n=0}^{N-1}{n}^{2}\left\{{\displaystyle \sum}_{i=0}^{N}{\displaystyle \sum}_{j=0}^{N-1}P\left(i,j\right)\right\}$ |

Homogeneity (I_{hmg}) | ${I}_{hmg}={\displaystyle \sum}_{i=0}^{N-1}{\displaystyle \sum}_{j=0}^{N-1}\frac{1}{1+{\left(i-j\right)}^{2}}P\left(i,j\right)$ |

Entropy (I_{Entr}) | ${I}_{Entr}=-{\displaystyle \sum}_{i=0}^{N-1}{\displaystyle \sum}_{j=0}^{N-1}P\left(i,j\right)\mathrm{log}P\left(i,j\right)$ |

Energy (I_{Enrg}) | ${I}_{Enrg}={\displaystyle \sum}_{i=0}^{N-1}{\displaystyle \sum}_{j=0}^{N-1}P{\left(i,j\right)}^{2}$ |

Correlation (I_{cor}) | ${I}_{cor}=\frac{{{\displaystyle \sum}}_{i=0}^{N-1}{{\displaystyle \sum}}_{j=0}^{N-1}\left(i,j\right)P\left(i,j\right)-{\mu}_{x}{\mu}_{y}}{{\sigma}_{x}{\sigma}_{y}}$ |

**Table 4.**Image features that were the most discriminant between dominant and non-dominant side (all subjects and only healthy subjects) and to determine subclinical tendinopathy.

Most Discriminant Features for Side Determination (Weight) | Most Discriminant Features for Side Determination (Only Healthy Subjects) (Weight) | Most Discriminant Features for Subclinical Tendinopathy Determination (Weight) | |||
---|---|---|---|---|---|

Kurtosis | (−65.0) | H. Homogeneity (45°) | (172.0) | H. Symmetry (0°) | (−24.5) |

H. Correlation (0°) | (58.9) | H. Contrast (45°) | (−155.8) | H. Contrast (45°) | (−14.3) |

H. Contrast (0°) | (12.9) | H. Symmetry (45°) | (−48.7) | H. Entropy (0°) | (14.2) |

Skewness | (−12.3) | H. Symmetry (0°) | (−29.7) | H. Correlation (45°) | (13.4) |

H. Entropy (0°) | (9.6) | H. Energy (0°) | (27.2) | H. Homogeneity (0°) | (5.5) |

H. Correlation (45°) | (7.0) | H. Correlation (0°) | (27.1) | Mean Intensity | (4.8) |

H. Energy (45°) | (−6.3) | H. Correlation (135°) | (6.1) | First−order Entropy | (4.3) |

H. Homogeneity (0°) | (3.5) | H. Energy (45°) | (−5.3) | Kurtosis | (−4.2) |

H. Entropy (45°) | (2.3) | Mean Intensity | (5.1) | Variance | (3.0) |

H. Symmetry (45°) | (−2.2) | H. Entropy (45°) | (4.8) | H. Correlation (0°) | (2.9) |

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## Share and Cite

**MDPI and ACS Style**

Meiburger, K.M.; Salvi, M.; Giacchino, M.; Acharya, U.R.; Minetto, M.A.; Caresio, C.; Molinari, F.
Quantitative Analysis of Patellar Tendon Abnormality in Asymptomatic Professional “Pallapugno” Players: A Texture-Based Ultrasound Approach. *Appl. Sci.* **2018**, *8*, 660.
https://doi.org/10.3390/app8050660

**AMA Style**

Meiburger KM, Salvi M, Giacchino M, Acharya UR, Minetto MA, Caresio C, Molinari F.
Quantitative Analysis of Patellar Tendon Abnormality in Asymptomatic Professional “Pallapugno” Players: A Texture-Based Ultrasound Approach. *Applied Sciences*. 2018; 8(5):660.
https://doi.org/10.3390/app8050660

**Chicago/Turabian Style**

Meiburger, Kristen M., Massimo Salvi, Maurizio Giacchino, U. Rajendra Acharya, Marco A. Minetto, Cristina Caresio, and Filippo Molinari.
2018. "Quantitative Analysis of Patellar Tendon Abnormality in Asymptomatic Professional “Pallapugno” Players: A Texture-Based Ultrasound Approach" *Applied Sciences* 8, no. 5: 660.
https://doi.org/10.3390/app8050660