Breast Image Registration Research Articles

Image matching using alpha-entropy measures and entropic graphs

Matching a reference image to a secondary image extracted from a database of transformed exemplars constitutes an important image retrieval task. Two related problems are: specification of a general class of discriminatory image features and an appropriate similarity measure to rank the closeness of the query to the database. In this paper we present a general method based on matching high dimensional image features, using entropic similarity measures that can be empirically estimated using entropic graphs such as the minimal spanning tree (MST). The entropic measures we consider are generalizations of the well-known Kullback–Liebler (KL) distance, the mutual information (MI) measure, and the Jensen difference. Our entropic graph approach has the advantage of being implementable for high dimensional feature spaces for which other entropy-based pattern matching methods are computationally difficult. We compare our technique to previous entropy matching methods for a variety of continuous and discrete features sets including: single pixel gray levels; tag sub-image features; and independent component analysis (ICA) features. We illustrate the methodology for multimodal face retrieval and ultrasound (US) breast image registration.

Biomechanical 3-D finite element modeling of the human breast using MRI data.

Breast tissue deformation modeling has recently gained considerable interest in various medical applications. A biomechanical model of the breast is presented using a finite element (FE) formulation. Emphasis is given to the modeling of breast tissue deformation which takes place in breast imaging procedures. The first step in implementing the FE modeling (FEM) procedure is mesh generation. For objects with irregular and complex geometries such as the breast, this step is one of the most difficult and tedious tasks. For FE mesh generation, two automated methods are presented which process MRI breast images to create a patient-specific mesh. The main components of the breast are adipose, fibroglandular and skin tissues. For modeling the adipose and fibroglandular tissues, we used eight noded hexahedral elements with hyperelastic properties, while for the skin, we chose four noded hyperelastic membrane elements. For model validation, an MR image of an agarose phantom was acquired and corresponding FE meshes were created. Based on assigned elasticity parameters, a numerical experiment was performed using the FE meshes, and good results were obtained. The model was also applied to a breast image registration problem of a volunteer's breast. Although qualitatively reasonable, further work is required to validate the results quantitatively.