The objective of this research is to model the mammographic parenchymal, ductal patterns and enhance the microcalcifications using deterministic fractal approach. According to the theory of deterministic fractal geometry, images can be modeled by deterministic fractal objects which are attractors of sets of two dimensional affine transformations. The Iterated Functions Systems and the Collage Theorem are the mathematical foundations of fractal image modeling. In this paper, a methodology based on fractal image modeling is developed to analyze and model breast background structures. We show that general mammographic parenchymal and ductal patterns can be well modeled by a set of parameters of affine transformations. Therefore, microcalcifications can be enhanced by taking the difference between the original image and the modeled image. Our results are compared with those of the partial wavelet reconstruction and morphological operation approaches. The results demonstrate that the fracta...

Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener--Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.

This paper proposes the use of an adaptive image enhancement system that implements the human visual (HVS) properties for the contrast enhancement of the X-Ray images. XRay images are of poor quality and usually interpreted visually. The HVS properties considered are the adaptive nature, multichannel, the highly non-linearity. The proposed method is adaptive, nonlinear, multichannel, and combins adaptive filters and homomorphic processing. Results presented illustrate the effectiveness of the proposed method.

Many imaging applications deal with the detection of small targets or spots embedded within an inhomogeneous background. We present a method that accomplishes a multiresolution detection on the wavelettransformed image. The targets are separated from the background by the exploitation of Renyi’s information, which is evaluated at the different decomposition levels of the wavelet transform. The scale-dependent candidate detections are successively combined by means of majority voting for final detection. Connections with results provided in different fields such as multifractal analysis, generalized information measures in scale-space, and cross-entropy analysis in fine-to-coarse transformations are discussed. Detection performance is investigated through an example from medical image analysis. © 2000 Optical Society of America [S0740-3232(00)02107-4] OCIS codes: 100.0100, 100.2000, 100.2960, 100.7410.