Complexity curve and grey level co‐occurrence matrix in the texture evaluation of breast tumor on ultrasound images

Abstract

This work aims at investigating texture parameters in distinguishing malign and benign breast tumors on ultrasound images. A rectangular region of interest (ROI) containing the tumor and its neighboring was defined for each image. Five parameters were extracted from the complexity curve (CC) of the ROI. Another five parameters were calculated from the grey-level co-occurrence matrix (GLCM) also for the ROI. The same was carried out for internal tumor region, hence, totaling 20 parameters. The linear discriminant analysis was applied to sets of up to five parameters and then the performances were assessed. The most relevant individual parameters were the contrast (con) (from the GLCM over the ROI) and the maximum value (mvi) from the CC just for the tumor internal region). When they were taken together, a correct classification slightly over 80% of the breast tumors was achieved. The highest performance (accuracy=84.2%, sensitivity=87.0%, and specificity=78.8%) was obtained with mvi, con, the standard deviation of the pixel pairs and the entropy, both for GLCM, and the internal region contrast also from GLCM. Parameters extracted from the internal region generally performed better and were more significant than those from the ROI. Moreover, parameters calculated only from CC or GLCM resulted in no statistically significant performance difference. These findings suggest that the texture parameters can be useful to help radiologist in distinguishing between benign or malign breast tumors on ultrasound images.

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

“…Since the introduction of GLCM by Haralick in the 1970s, 23 GLCM has been widely used as texture measurements in medical imaging. Over the years, many researchers have used GLCM texture analysis of ultrasound images for cancer diagnoses in various organs, ranging from the liver 24 and breast [25][26][27][28][29][30] to the parotid gland. 31 Our work is the first to investigate the GLCM textural features for the quantitative evaluation of radiation-induced parotid gland injury.…”

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

“…The local texture features are also effective in differentiating benign and malignant breast tumors [8][9][10][11][12][13][14]. When these features are used to describe the lesion, the lesion can be viewed as a bag and the subregions of the lesion can be viewed as instances of the bag.…”

“…Different tissues have different textures; therefore, the texture of BUS image is an effective feature for differentiating benign and malignant breast tumors [3,8]. Auto-covariance [9], fractal dimension [10], co-occurrence matrix [11], runlength matrix [12], and wavelet coefficients [13] have been widely utilized to derive discriminant features.…”

Assessing Response to Chemoradiotherapy on 18F-FDG PET Images in Non-Small Cell Lung Cancer using New Approaches of Histogram and Gray Level Co-Occurrence Matrix

“…Ratio of gray value distribution in every level among the range of level 11~32 in Figure 3b has an obvious increase comparing Figure 3a and has almost more than one time increase in level 18~32. Then we divided the 32 discrete gray level intervals into three parts as follows: The lower (level 1-10), the middle (level [11][12][13][14][15][16][17][18][19][20][21][22] and the higher (level 23-32). The percentage of each part ratio variation between pretreatment and treatment completion were the lower part (pro: 62.9%-pre: 84.7%) -21.8%, the middle part (28.9%-11%) -17.9% and higher part (8.2%-4.3%) -3.9% for the example patient.…”

“…We extract texture parameters using Gray Level Co-occurrence Matrix (GLCM) [17,18], which is a classical algorithm in texture analysis. The following Several different texture parameters were computed.…”