title: Texture feature analysis for breast ultrasound image enhancement.
abstract: Texture analysis of breast ultrasound B-scans has been widely applied to the segmentation and classification of breast tumors. We present a parametric imaging method based on the texture features to preserve tumor edges and retain the texture information simultaneously. Four texture-feature parameters--homogeneity, contrast, energy and variance--were evaluated using the gray-level co-occurrence matrix. The local texture-feature parameter was assigned as the new pixel located at the center of the sliding window at each position. This process yielded the texture-feature parametric image as the map of texture-feature values. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were estimated to show the quality improvement of the images. The contours outlined from 11 experienced physicians and the gradient vector flow (GVF) snake algorithm segmentations were adopted to verify the edge enhancement of texture-feature parametric images. In addition, the Fisher's linear discriminant analysis (FLDA) and receiver-operating-characteristic (ROC) curve were used to test the performance of breast tumor classifications between texture-feature parametric images and B-scan images. The results show that the variance images have higher CNR and SNR estimates than those in the B-scan images. There was a high agreement between the physician's manual contours and the GVF snake automatic segmentations in the variance images, and the mean area overlap was over 93%. The area under the ROC curve from the B-scan images had 0.81 and 95% confidence interval of 0.72-0.88, and the texture-feature parametric images had 0.90 and 95% confidence interval of 0.84-0.96. These findings indicate that the texture-feature parametric imaging method can be not only useful for determining the location of the lesion boundary but also as a tool to improve the accuracy of breast tumor classifications.
authors.affiliation: Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.
authors.forename: Yin-Yin
authors.initials: YY
authors.lastname: Liao
authors.forename: Jui-Chen
authors.initials: JC
authors.lastname: Wu
authors.forename: Chia-Hui
authors.initials: CH
authors.lastname: Li
authors.forename: Chih-Kuang
authors.initials: CK
authors.lastname: Yeh
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D012372:ROC Curve; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult