ABSTRACT
X-ray mammograms and MR volumes provide complementary information for early breast cancer diagnosis. The breast is deformed during mammography, therefore the images can not be compared directly. A registration algorithm is investigated to fuse the images automatically. A finite element simulation was applied to a MR image of an underformed breast and compared to a compressed breast using different tissue models and boundary conditions. Based on the results a set of patient data was registered. To archive the requested accuracy distinguishing between the different tissue types of the breast was not necessary. A linear elastic model was sufficient. It was possible to simulate the deformation with an average deviation of approximately of the size of a voxel in the MRI data and retrieve the position of a lesion with an error of 3.8 mm in the patient data.
Subject(s)
Breast Neoplasms/diagnosis , Breast/pathology , Diagnosis, Computer-Assisted , Image Processing, Computer-Assisted , Imaging, Three-Dimensional , Magnetic Resonance Imaging , Mammography , Female , Finite Element Analysis , Humans , Phantoms, ImagingABSTRACT
Major problems in treating breast cancer are the early detection of tumors and accurate biopsy of small volumes of breast (mamma) tissue. This report presents an elastic registration algorithm of two x-ray mammograms and a corresponding magnetic resonance imaging (MRI) volume. To cope with the soft tissue deformation of the breast during mammography, a two-dimensional model of breast deformation behavior is used as an elastic transformation. Normalized mutual information is employed as a measure of similarity. Regions of interest in the uncompressed x-ray mammograms are projected into the MRI volume to determine their three-dimensional origin.
Subject(s)
Algorithms , Image Processing, Computer-Assisted , Imaging, Three-Dimensional , Magnetic Resonance Imaging , Mammography , Female , Humans , Image Processing, Computer-Assisted/methods , Phantoms, ImagingSubject(s)
Breast Neoplasms/diagnostic imaging , Diagnosis, Computer-Assisted/instrumentation , Image Processing, Computer-Assisted/instrumentation , Mammography/instrumentation , Neural Networks, Computer , Calcinosis/diagnostic imaging , Computer Systems , Female , Humans , Radiographic Image Interpretation, Computer-Assisted/instrumentation , Sensitivity and SpecificityABSTRACT
Neural network and statistical classification methods were applied to derive an objective grading for moderately and poorly differentiated lesions of the prostate, based on characteristics of the nuclear placement patterns. A partly trained multilayer neural network was used as a feature extractor. A hybrid classifier system using a quadratic Bayesian classifier applied to these features allowed grade assignment consensus with visual diagnosis in 96% of fields from a training set of 500 fields and in 77% of 130 fields of a test set.
