This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.

Deep learning-based automatic classification of breast tumors using parametric imaging techniques from ultrasound (US) B-mode images is still an exciting research area. The Rician inverse Gaussian (RiIG) distribution is currently emerging as an appropriate example of statistical modeling. This study presents a new approach of correlated-weighted contourlet-transformed RiIG (CWCtr-RiIG) and curvelet-transformed RiIG (CWCrv-RiIG) image-based deep convolutional neural network (CNN) architecture for breast tumor classification from B-mode ultrasound images. A comparative study with other statistical models, such as Nakagami and normal inverse Gaussian (NIG) distributions, is also experienced here. The weighted entitled here is for weighting the contourlet and curvelet sub-band coefficient images by correlation with their corresponding RiIG statistically modeled images. By taking into account three freely accessible datasets (Mendeley, UDIAT, and BUSI), it is demonstrated that the proposed approach can provide more than 98 percent accuracy, sensitivity, specificity, NPV, and PPV values using the CWCtr-RiIG images. On the same datasets, the suggested method offers superior classification performance to several other existing strategies.

Medical ultrasound images are inherently corrupted with speckle noise. The knowledge of the statistics of the speckle is necessary for developing effective methods to reduce speckle noise. In this paper, the Bessel K-Form (BKF) probability density function (PDF) is proposed to model the logarithmically transformed speckle noise. A maximum likelihood -based method is developed for estimating the BKF parameters. The appropriateness of the BKF distribution in modeling the speckle is studied for different noise levels in the wavelet and curvelet transform domains. It is shown that the BKF can capture the noise statistics better than the Gaussian and normal inverse Gaussian PDFs.

Medical ultrasound images are inherently corrupted with speckle noise in a multiplicative manner. The most popular approach of despeckling is homomorphic filtering, in which the multiplicative speckle noise converted to an additive one by log-transformation. The knowledge of the statistics of the log-transformed speckle is necessary for developing effective methods for speckle reduction. The Bessel K-Form (BKF) probability density function (pdf) is proposed to model the logarithmically transformed speckle noise in the dual-tree complex wavelet transform (DT-CWT) domain. A maximum likelihood based method is developed for estimating the parameters of the BKF pdf. The suitability of the BKF pdf in modeling the speckle is studied for different noise levels in the 2D DT-CWT domain. It is shown that the BKF can capture the statistics of the DT-CWT coefficients corresponding to log-transformed speckle better than the Gaussian and normal inverse Gaussian pdfs.

Speckle noise is an inherent phenomenon in medical ultrasound (US) images. Since it degrades an ultrasound image quality and reduces its diagnostic value, reduction of speckle noise is a very important pre-processing step in ultrasound image processing. For this purpose, the knowledge of the statistics of speckle noise is necessary; especially in the multi-resolution transform domain due to their sparse and efficient representation of images. In this paper a Bessel K-Form (BKF) probability density function (pdf) is proposed as a highly suitable prior for modeling the log-transformed speckle noise in the well-known contourlet transform domain. A maximum likelihood based method is presented for estimating the parameters of the BKF pdf. The appropriateness of the BKF pdf in modeling the speckle is studied for different noise levels in the contourlet transform domain, in addition the suitability of BKF model is investigated for the case of real US images. It is shown that, in general the BKF can model the statistics of the contourlet transform coefficients corresponding to log-transformed speckle better than the traditional Gaussian and normal inverse Gaussian pdfs.