A non-linear morphometric feature selection approach for breast tumor contour from ultrasonic images

Abstract

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.

Breast ultrasound lesions recognition: end-to-end deep learning approaches

“…Among different proposed approaches considering solid mass classification, there are two main feature descriptors, 27 i.e., echo texture 11,28 and shape and margin features. 29 We present a couple of works on multistage machine learning methods. For a full review, please refer to Cheng et al 26 Liu et al 30 proposed a breast classification system for color Doppler flow imaging and B-mode ultrasound.…”

Preliminary study of the technical limitations of automated breast ultrasound: from procedure to diagnosis

“…Nevertheless, the interobserver agreement kappa indices described in the literature range from 0.28 to 0.83 for a diagnosis essentially based on the real-time subjective evaluation of the morphological aspects of a lesion (10)(11)(12) . Aiming to reduce the number of biopsies taken from benign solid tumors and increase the consistency of the diagnostic interpretation of ultrasound, several studies have proposed methods to aid in the diagnosis of breast cancer (15,(20)(21)(22)(23)(24) .…”

“…However, because of the high demand for breast ultrasound, ultrasound examinations of the breast are often performed by general radiologists or ultrasonographers with limited experience in breast imaging, rather than breast specialists, which reduces the sensitivity of the method and increases the number of false-positive results ( 9 - 13 ) . The growing concern of doctors and patients about the increased risk of breast cancer related to high breast density, combined with the limitations of mammography in dense breasts, has led to the development of additional screening tools, automated breast ultrasound (ABUS) being one such tool ( 13 - 15 ) .…”

Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound

“…The infiltrative nature of malignant tumors generate an irregular pattern of impedance discontinuities, which results irregular, spiculated or ill-defined boundary in breast ultrasound images. However, benign tumors have a more uniform growth with smooth, round, and well-defined boundaries [9]. Based on this, Wu et al [17,18] combined textural and morphological features and achieved an A Z value of 0.9614.…”

“…It is essential to quantify the characteristics of breast tumors for the detection as well as discrimination, which are quite often difficult to grasp due to intrinsic limitations of the ultrasound imaging process, such as low contrast, speckle noise, heterogeneity or artifacts. It is significant to explore the feature, or set of features, which provide better quantifications of the characteristics of tumors [9]. Some general guidelines [10] for identifying significant features which leads to accurate diagnosis are discrimination, reliability, independence and optimality.…”