Denoising and enhancement of mammographic images under the assumption of heteroscedastic additive noise by an optimal subband thresholding

Mammographic images suffer from low contrast and signal dependent noise, and a very small size of tumoral signs is not easily detected, especially for an early diagnosis of breast cancer. In this context, many methods proposed in literature fail for lack of generality. In particular, too weak assumptions on the noise model, e.g., stationary normal additive noise, and an inaccurate choice of the wavelet family that is applied, can lead to an information loss, noise emphasizing, unacceptable enhancement results, or in turn an unwanted distortion of the original image aspect. In this paper, we consider an optimal wavelet thresholding, in the context of Discrete Dyadic Wavelet Transforms, by directly relating all the parameters involved in both denoising and contrast enhancement to signal dependent noise variance (estimated by a robust algorithm) and to the size of cancer signs. Moreover, by performing a reconstruction from a zero-approximation in conjunction with a Gaussian smoothing filter, we are able to extract the background and the foreground of the image separately, as to compute suitable contrast improvement indexes. The whole procedure will be tested on high resolution X-ray mammographic images and compared with other techniques. Anyway, the visual assessment of the results by an expert radiologist will be also considered as a subjective evaluation

Uncertainty handling and propagation in X-ray images analysis systems by means of random fuzzy variables

In this paper we consider uncertainty handling and propagation by means of RFV through a Computer Aided Detection (CAD) system for denoising and contrast enhancement of mammographic images. In this context, we assume that uncertainty associated to each pixel of the image has both a random and a not negligible systematic contribution. So, after a noise variance estimation performed on the original image, using recent RFV mathematics, we propagate uncertainty through the whole system. Finally, we compare our results with those obtained by a Montecarlo simulation

Uncertainty propagation for the assessment of tumoral masses segmentation

abstract: In this paper, we perform the assessment of a tumoral mass segmentation and characterization algorithm by implementing the uncertainty propagation through the blocks. We use a Monte Carlo method owing to the iterative and very complex structure of the algorithms used. The validation of the results is based on confidence intervals for given coverage probabilities and ad hoc performance metrics