A numerical realistic 3D anthropomorphic breast model is useful for evaluating breast imaging applications. A method is proposed to model small and medium-scale fibroglandular and intra-glandular adipose tissues observed in the center part of clinical breast CT images. The method builds upon a previously proposed model formulated as stochastic geometric processes with mathematically tractable parameters. In this work, the medium-scale parameters were automatically and objectively inferred from breast CT images. We hypothesized that a set of random ellipsoids exhibiting cluster interaction is representative to model the medium-scale intra-glandular adipose compartments. The ellipsoids were reconstructed using a multiple birth, death and shift algorithm. Then, a Matérn cluster process was used to fit the reconstructed ellipsoid centers. Finally, distributions of the ellipsoid shapes and orientations were estimated using maximum likelihood estimators. Feasibility was demonstrated on 16 volumes of interests (VOI). To assess the realism of the 3D breast texture model, β and LFE metrics computed in simulated projection images of simulated texture realizations and clinical images were compared. Visual realism was illustrated. For 12 out of 16 VOIs, our hypothesis on clustering interaction process is confirmed. The average β values from simulated texture images (3.7 to 4.2) of the 12 different VOIs are higher than the average β value from 2D clinical images (2.87). LFE of simulated texture images and clinical mammograms are similar. Compared to our previous model, whereby simulation parameters were based upon empirical observations, our inference method substantially augments the ability to generate textures with higher visual realism and larger morphological variety.

The possibility to provide an adequate task assistance using underactuated robots for human-robot tool comanipulation is investigated. This novel approach does not take into account any a priori knowledge about user depending parameters however optimizes the robot-user synergy, for instance during US breast examinations. Results show that the examination time can be reduced and a tendency for increasing scanning accuracy using underactuated robots.

This paper presents a novel needle path planning method for biopsy guided by digital breast tomosynthesis (DBT) taking into account breast heterogeneity. First, a multi-resolution optimization approach, guaranteeing that a relevant path is computed, is proposed. Moreover, local breast mechanical parameters required for a heterogeneous finite element simulation, are extracted from the DBT data. The proposed approach computes a 3D local breast glandularity estimation used for Young's modulus determination. This planning method, using a heterogeneous model, reduces the tool positioning error meanly by 75%. In addition, the results show that a breast heterogeneous model has the potential to improve planning accuracy.

This paper presents a new needle path planning method for digital breast tomosynthesis biopsy. Needle insertion planning into deformable tissue for breast biopsy procedure is a challenging task because of the infinite possibilities of insertion points. In addition, the lesion moves from its original position when the radiologist introduces the biopsy needle. The proposed approach couples Rapidly-exploring Random Trees with Finite Element Simulation in order to find an optimal path taking breast deformations into account. Simulation results show that this method reduces the error (i.e. the distance between the needle tip and the lesion) by 80%.

Fuzzy connected filters were recently introduced as an extension of connected filters within the fuzzy set framework. They rely on the representation of the image gray levels by fuzzy quantities, which are suitable to represent imprecision usually contained in images. No robust construction method of these fuzzy images has been introduced so far. In this paper we propose a generic method to fuzzify a crisp image in order to explicitly take imprecision on grey levels into account. This method is based on the conversion of statistical noise present in an image, which cannot be directly represented by fuzzy sets, into a denoising imprecision. The detectability of constant gray level structures in these fuzzy images is also discussed.

We have developed a new algorithm for the characterization of microcalcification clusters. Fuzzy logic is well suited to represent and to manipulate data and knowledge at different levels of the algorithm. Our algorithm is built in 3 steps: Detection and segmentation of the individual microcalcifications, measurements on the segmented microcalcifications (shape, contrast, relative localization), use of these measurements as inputs of a learning system which concludes if the current case is malignant or not. We first describe some aspects of the fuzzy segmentation we have implemented. Then we explain how we build fuzzy measurements from the segmented objects and how these measurements are manipulated into the fuzzy decision tree we are using. Finally, we present the preliminary results we obtained with our test database. © 1997 John Wiley & Sons, Inc.