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Described here is a novel texture extraction method based on auto-mutual information (AMI) for classifying breast lesions. The objective is to extract discriminating information found in the non-linear relationship of textures in breast ultrasound (BUS) images. The AMI method performs three basic tasks: (i) it transforms the input image using the ranklet transform to handle intensity variations of BUS images acquired with distinct ultrasound scanners; (ii) it extracts the AMI-based texture features in the horizontal and vertical directions from each ranklet image; and (iii) it classifies the breast lesions into benign and malignant classes, in which a support-vector machine is used as the underlying classifier. The image data set is composed of 2050 BUS images consisting of 1347 benign and 703 malignant tumors. Additionally, nine commonly used texture extraction methods proposed in the literature for BUS analysis are compared with the AMI method. The bootstrap method, which considers 1000 bootstrap samples, is used to evaluate classification performance. The experimental results indicate that the proposed approach outperforms its counterparts in terms of area under the receiver operating characteristic curve, sensitivity, specificity and Matthews correlation coefficient, with values of 0.82, 0.80, 0.85 and 0.63, respectively. These results suggest that the AMI method is suitable for breast lesion classification systems., (Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Purpose: This paper presents an adaptive contrast enhancement method based on sigmoidal mapping function (SACE) used for improving the computerized segmentation of breast lesions on ultrasound., Methods: First, from the original ultrasound image an intensity variation map is obtained, which is used to generate local sigmoidal mapping functions related to distinct contextual regions. Then, a bilinear interpolation scheme is used to transform every original pixel to a new gray level value. Also, four contrast enhancement techniques widely used in breast ultrasound enhancement are implemented: histogram equalization (HEQ), contrast limited adaptive histogram equalization (CLAHE), fuzzy enhancement (FEN), and sigmoid based enhancement (SEN). In addition, these contrast enhancement techniques are considered in a computerized lesion segmentation scheme based on watershed transformation. The performance comparison among techniques is assessed in terms of both the quality of contrast enhancement and the segmentation accuracy. The former is quantified by the measure, where the greater the value, the better the contrast enhancement, whereas the latter is calculated by the Jaccard index, which should tend towards unity to indicate adequate segmentation., Results: The experiments consider a data set with 500 breast ultrasound images. The results show that SACE outperforms its counterparts, where the median values for the measure are: SACE: 139.4, SEN: 68.2, HEQ: 64.1, CLAHE: 62.8, and FEN: 7.9. Considering the segmentation performance results, the SACE method presents the largest accuracy, where the median values for the Jaccard index are: SACE: 0.81, FEN: 0.80, CLAHE: 0.79, HEQ: 77, and SEN: 0.63., Conclusion: The SACE method performs well due to the combination of three elements: (1) the intensity variation map reduces intensity variations that could distort the real response of the mapping function, (2) the sigmoidal mapping function enhances the gray level range where the transition between lesion and background is found, and (3) the adaptive enhancing scheme for coping with local contrasts. Hence, the SACE approach is appropriate for enhancing contrast before computerized lesion segmentation., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Breast ultrasound (BUS) is considered the most important adjunct method to mammography for diagnosing cancer. However, this image modality suffers from an intrinsic artifact called speckle noise, which degrades spatial and contrast resolution and obscures the screened anatomy. Hence, it is necessary to reduce speckle artifacts before performing image analysis by means of computer-aided diagnosis systems, for example. In addition, the trade-off between smoothing level and preservation of lesion contour details should be addressed by speckle reduction schemes. In this scenario, we propose a BUS despeckling method based on anisotropic diffusion guided by Log-Gabor filters (ADLG). Because we assume that different breast tissues have distinct textures, in our approach we perform a multichannel decomposition of the BUS image using Log-Gabor filters. Next, the conduction coefficient of anisotropic diffusion filtering is computed using texture responses instead of intensity values as stated originally. The proposed algorithm is validated using both synthetic and real breast data sets, with 900 and 50 images, respectively. The performance measures are compared with four existing speckle reduction schemes based on anisotropic diffusion: conventional anisotropic diffusion filtering (CADF), speckle-reducing anisotropic diffusion (SRAD), texture-oriented anisotropic diffusion (TOAD), and interference-based speckle filtering followed by anisotropic diffusion (ISFAD). The validity metrics are the Pratt's figure of merit, for synthetic images, and the mean radial distance (in pixels), for real sonographies. Figure of merit and mean radial distance indices should tend toward '1' and '0', respectively, to indicate adequate edge preservation. The results suggest that ADLG outperforms the four speckle removal filters compared with respect to simulated and real BUS images. For each method--ADLG, CADF, SRAD, TOAD and ISFAD--the figure of merit median values are 0.83, 0.40, 0.39, 0.51 and 0.59, and the mean radial distance median results are 4.19, 6.29, 6.39, 6.43 and 5.88., (Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Purpose: This work aims to investigate the combination of morphological and texture parameters in distinguishing between malignant and benign breast tumors in ultrasound images., Methods: Linear discriminant analysis was applied to sets of up to five parameters, and then the performances were assessed using the area A(z) (± standard error) under the receiver operator characteristic curve, accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value, and negative predictive value., Results: The most relevant individual parameter was the normalized residual value (nrv), calculated from the convex polygon technique. The best performance among all studied combinations was achieved by two morphological and three texture parameters (nrv, con, std, R, and asm(i)), which correctly distinguished nearly 85% of the breast tumors., Conclusions: This result indicates that the combination of morphological and texture parameters may be useful to assist physicians in the diagnostic process, especially if it is associated with an automatic classification tool.

In this paper, we investigated the behavior of 22 co-occurrence statistics combined to six gray-scale quantization levels to classify breast lesions on ultrasound (BUS) images. The database of 436 BUS images used in this investigation was formed by 217 carcinoma and 219 benign lesions images. The region delimited by a minimum bounding rectangle around the lesion was employed to calculate the gray-level co-occurrence matrix (GLCM). Next, 22 co-occurrence statistics were computed regarding six quantization levels (8, 16, 32, 64, 128, and 256), four orientations (0° , 45° , 90° , and 135°), and ten distances (1, 2,...,10 pixels). Also, to reduce feature space dimensionality, texture descriptors of the same distance were averaged over all orientations, which is a common practice in the literature. Thereafter, the feature space was ranked using mutual information technique with minimal-redundancy-maximal-relevance (mRMR) criterion. Fisher linear discriminant analysis (FLDA) was applied to assess the discrimination power of texture features, by adding the first m-ranked features to the classification procedure iteratively until all of them were considered. The area under ROC curve (AUC) was used as figure of merit to measure the performance of the classifier. It was observed that averaging texture descriptors of a same distance impacts negatively the classification performance, since the best AUC of 0.81 was achieved with 32 gray levels and 109 features. On the other hand, regarding the single texture features (i.e., without averaging procedure), the quantization level does not impact the discrimination power, since AUC = 0.87 was obtained for the six quantization levels. Moreover, the number of features was reduced (between 17 and 24 features). The texture descriptors that contributed notably to distinguish breast lesions were contrast and correlation computed from GLCMs with orientation of 90° and distance more than five pixels.

Ultrasound breast images have been used to improve diagnostics and decrease the number of unneeded biopsies. Malignant breast tumors tend to present irregular and blurred contours while benign ones are usually round, smooth and well-defined. Accordingly, investigating the tumor contour may help in establishing diagnosis. Herein, Mutual Information and Linear Discriminant Analysis were implemented to rank morphometric features in discriminating breast tumors in ultrasound images. Seven features were extracted from Convex Polygon and the Normalized Radial Length techniques. By applying a Mutual Information based approach, it was possible to identity features with possibly non-linear contributions to the outcome., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Purpose: To use the BI-RADS ultrasound classification in an intraobserver retrospective study of the interpretation of breast images., Materials and Methods: The study used 40 breast ultrasound images recorded in orthogonal planes, obtained from patients with an indication for surgery. Eight professionals experienced in breast imaging analysis retrospectively reviewed these lesions, in three rounds of image interpretation (with a 3-6 months interval between rounds). Observers had no access to information from medical records or histopathological results, and, without their knowledge, in each new round were assigned the same images previously interpreted by them. Fleiss-modified Kappa measures were the study main concordance index. Besides the BI-RADS, a scale grouping its categories 2-3 and 4-5 was also used. The statistical analysis concerned the intraobserver agreement., Results: Kappa values ranged from 0.37 to 0.75 (original categories) and from 0.73 to 0.87 (grouped categories). Overall, out of the 8 observers, 7 presented moderate to substantial concordance (Kappa values 0.51 to 0.74)., Conclusion: The BI-RADS is a reporting tool that provides a standardized terminology for US exams. In this study, moderate to substantial concordance in Kappa values was found, in agreement with other studies of the literature., (Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.)

Purpose: This paper presents a computerized segmentation method for breast lesions on ultrasound (US) images., Methods: It consists of first applying a contrast-enhanced approach, i.e., a contrast-limited adaptive histogram equalization. Then, aiming at removing speckle and enhancing the lesion boundary, an anisotropic diffusion filter, guided by texture descriptors derived from a set of Gabor filters, is applied. To eliminate the distant pixels that do not belong to the tumor, the resulting filtered image is multiplied by a constraint Gaussian function. By doing so, both the segmentation and the marker functions are generated and could be used in the marker-controlled watershed transformation algorithm to create potential lesion boundaries. Finally, to determine the lesion contour, the average radial derivative function is evaluated. The proposed method was tested with 50 breast US images and 60 simulated "ultrasound-like" images. Accuracy and precision of the segmentation method were then assessed. For the accuracy, three parameters were used: Overlap ratio (OR), normalized residual value (nrv), and proportional distance (PD) between contours., Results: The average results for US images were OR = 0.86 +/- 0.05, nrv = 0.16 +/- 0.06, and PD = 6.58 +/- 2.52%. For simulated ultrasound-like images, a better performance (OR = 0.92 +/- 0.01, nrv = 0.08 +/- 0.01, and PD = 3.20 +/- 0.53%) was achieved., Conclusions: The segmentation method proposed was capable of delineating the lesion contours with high accuracy in comparison to both the radiologists' delineations and the true delineations of simulated images. Moreover, this method was also found to be robust to human-dependent parameters variations.

This work aims at investigating seven morphological parameters in distinguishing malignant and benign breast tumors on ultrasound images. Linear discriminant analysis was applied to sets of up to five parameters and then the performances were assessed using the area Az (+/- standard error) under the ROC curve, accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value and negative predictive value. The most relevant individual parameters were the normalized residual value (nrv) and overlap ratio (RS), both calculated from the convex polygon technique, and the circularity (C). When nrv and C were taken together with roughness (R), calculated from normalized radial length (NRL), a performance slightly over 83% in distinguishing malignant and benign breast tumors was achieved., ((c) 2009 IPEM. Published by Elsevier Ltd. All rights reserved.)