KEYWORDS: 3D modeling, Breast, Finite element methods, Mammography, Magnetic resonance imaging, 3D image processing, X-rays, Mathematical modeling, Tissues, X-ray imaging
Predicting breast tissue deformation is of great significance in several medical applications such as biopsy, diagnosis, and surgery. In breast surgery, surgeons are often concerned with a specific portion of the breast, e.g., tumor, which must be located accurately beforehand. Also clinically it is important for combining the information provided by images from several modalities or at different times, for the detection/diagnosis, treatment planning and guidance of interventions. Multi-modality imaging of the breast obtained by X-ray mammography, MRI is thought to be best achieved through some form of data fusion technique. However, images taken by these various techniques are often obtained under entirely different tissue configurations, compression, orientation or body position. In these cases some form of spatial transformation of image data from one geometry to another is required such that the tissues are represented in an equivalent configuration.
We propose to use a 3D finite element model for lesion correspondence in breast imaging. The novelty of the approach lies in the following facts: (1) Finite element is the most accurate technique for modeling deformable objects such as breast. The physical soundness and mathematical rigor of finite element method ensure the accuracy and reliability of breast modeling that is essential for lesion correspondence. (2) When both MR and mammographic images are available, a subject-specific 3D breast model will be built from MRIs. If only mammography is available, a generic breast model will be used for two-view mammography reading. (3) Incremental contact simulation of breast compression allows accurate capture of breast deformation and ensures the quality of lesion correspondence. (4) Balance between efficiency and accuracy is achieved through adaptive meshing. We have done intensive research based on phantom and patient data.
Mammography is the most effective method for early detection of breast cancer. However, the positive predictive value for classification of malignant and benign lesion from mammographic images is not very high. Clinical studies have shown that most biopsies for cancer are very low, between 15% and 30%. It is important to increase the diagnostic accuracy by improving the positive predictive value to reduce the number of unnecessary biopsies. In this paper, a new classification method was proposed to distinguish malignant from benign masses in mammography by Support Vector Machine (SVM) method. Thirteen features were selected based on receiver operating characteristic (ROC) analysis of classification using individual feature. These features include four shape features, two gradient features and seven Laws features. With these features, SVM was used to classify the masses into two categories, benign and malignant, in which a Gaussian kernel and sequential minimal optimization learning technique are performed. The data set used in this study consists of 193 cases, in which there are 96 benign cases and 97 malignant cases. The leave-one-out evaluation of SVM classifier was taken. The results show that the positive predict value of the presented method is 81.6% with the sensitivity of 83.7% and the false-positive rate of 30.2%. It demonstrated that the SVM-based classifier is effective in mass classification.
Mass segmentation is a vital step in CAD mass detection and classification. A challenge for mass segmentation in mammograms is that masses may contact with some surrounding tissues, which have the similar intensity. In this paper, a novel graph-based algorithm has been proposed to segment masses in mammograms. In the proposed algorithm, the procedure of region growing is represented as a growing tree whose root is the selected seed. Active leaves, which have the ability to grow, in the connection area between adjacent regions are deleted to stop growing, then separating the adjacent regions while keeping the spiculation of masses, which is a primary sign of malignancy for masses. The new constrained segmentation was tested with 20 cases in USF moffitt mammography database against the conventional region growing algorithm. The segmented mass regions were evaluated in terms of the overlap area with annotations made by the radiologist. We found that the new graph-based segmentation more closely match radiologists' outlines of these masses.
KEYWORDS: Breast, Image segmentation, Magnetic resonance imaging, Mammography, Tissues, 3D visualizations, Algorithm development, 3D image processing, Chest, Image processing algorithms and systems
MRI mammography has a number of advantages, including the tomographic, and therefore three-dimensional (3-D) nature, of the images. It allows the application of MRI mammography to breasts with dense tissue, post operative scarring, and silicon implants. However, due to the vast quantity of images and subtlety of difference in MR sequence, there is a need for reliable computer diagnosis to reduce the radiologist's workload. The purpose of this work was to develop automatic breast/tissue segmentation and visualization algorithms to aid physicians in detecting and observing abnormalities in breast. Two segmentation algorithms were developed: one for breast segmentation, the other for glandular tissue segmentation. In breast segmentation, the MRI image is first segmented using an adaptive growing clustering method. Two tracing algorithms were then developed to refine the breast air and chest wall boundaries of breast. The glandular tissue segmentation was performed using an adaptive thresholding method, in which the threshold value was spatially adaptive using a sliding window. The 3D visualization of the segmented 2D slices of MRI mammography was implemented under IDL environment. The breast and glandular tissue rendering, slicing and animation were displayed.
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