Ultrasound GLCM texture analysis of radiation‐induced parotid‐gland injury in head‐and‐neck cancer radiotherapy: An in vivo study of late toxicity

Abstract

Purpose: Xerostomia (dry mouth), secondary to irradiation of the parotid glands, is one of the most common side effects of head-and-neck cancer radiotherapy. Diagnostic tools able to accurately and efficiently measure parotid gland injury have yet to be introduced into the clinic. This study's purpose is to investigate sonographic textural features as potential imaging signatures for quantitative assessment of parotid-gland injury after head-and-neck radiotherapy. Methods:The authors have investigated a series of sonographic features obtained from the gray level co-occurrence matrix (GLCM) -a second order statistical method of texture analysis. These GLCM textural features were selected based on empirical observations that the normal parotid gland exhibits homogeneous echotexture, whereas the postradiotherapy parotid gland often exhibits heterogeneous echotexture. We employed eight sonographic features: (1) angular second moment (ASM), (2) inverse differential moment (IDM), (3) contrast, (4) variance, (5) correlation, (6) entropy, (7) cluster shade, and (8) cluster prominence. Altogether, sonographic properties of the parotid glands were quantified by their degrees of homogeneity (ASM and IDM), heterogeneity (contrast and variance), smoothness (correlation), randomness (entropy), and symmetry (cluster shade and prominence). The sonographic features were tested in a pilot study of 12 postradiotherapy patients and 7 healthy volunteers. The mean follow-up time for the postradiotherapy patients was 17.2 months (range: 12.1-23.9 months) and the mean radiation dose to the parotid glands was 32.3 Gy (range: 11.0-63.4 Gy). Each participant underwent one ultrasound study in which longitudinal (vertical) ultrasound scans were performed on the bilateral parotids -a total of 24 postirradiation and 14 normal parotid glands were examined. The 14 normal parotid glands served as the control group. A radiologist contoured the parotid glands on the B-mode images and the sonographic features were computed from the contoured region-ofinterest. Results: The authors observed significant differences (p < 0.05) in all sonographic features between the normal and postradiotherapy parotid glands. The sonographic findings were consistent with the clinical observations of the ultrasound images: normal parotid glands exhibited homogeneous texture, while the postradiotherapy parotid glands exhibited heterogeneous echotexture (e.g., hyperechoic lines and spots), which likely represents fibrosis. Conclusions:The authors have demonstrated the feasibility of ultrasonic texture evaluation of parotid glands; and the sonographic features may serve as imaging signatures to assess radiation-induced parotid injury.

Texture analysis of medical images for radiotherapy applications

“…The early prediction of these events can be of interest both to better plan radiation treatment, by considering anatomical variations of the glands and to identify those patients who can benefit from personalized care. In this case, echographic 101 and CT 1022104 images of the parotid tissue were examined and GLCM features and fractal dimension were calculated. On echographic images, an increased entropy was observed with respect to normal glands, whereas on CT images, a decrease in mean CT density, entropy and fractal dimension was found in the first half of RT.…”

A quantitative method for measurement of HL-60 cell apoptosis based on diffraction imaging flow cytometry technique

“…An examination of the GLCM parameter definitions [23,31] reveals that CLS measures the skewness of a texture pattern for gauging the perceptual concepts of uniformity while COR characterizes the degree of correlation in the paired pixels on their gray levels [30]. On the other hand, CON quantifies the local pattern variations presented in an image, which correlates highly with the difference between the lowest and highest values of pixel intensities within a region of interest [33]. DIS is another feature parameter to describe the contrast of the image besides the CON parameter.…”

Image Evaluation and Association Analysis of the Cardiovascular Disease of the Degree of Pancreatic Steatosis in Ultrasonography

“…Using the histogram equalization and the filter range for the noise reduction effect to improve the contrast of the image as preprocessing method of an ultrasound image we increased the recognition rate classification. GLCM algorithm was applied to highlight the texture characteristics of the image [8]. A characteristic parameters of GLCM algorithm, namely Autocorrelation, Sum of squares, and Sum average, were extracted for Sum of variance of the dispersion, expresseing it as a value of the texture features [9].…”