Robust methods for precise segmentation of breast region or volume from breast X-ray images, including mammogram and tomosynthetic image, is crucial for applications of these medical images. However, this task is challenging because the acquired images not only are inherent noisy and inhomogeneous, but there are also connected or overlapped artifacts, or noises on the images as well, due to local volume effect of tissues, parametric resolutions and other physical limitations of the imaging device. This paper proposes and develops robust fuzzy c-means (FCM) segmentation methods for segmentation of breast region on breast x-ray images, including mammography and tomosynthesis, respectively. We develop spatial information- and kernel function- based FCM methods to differentiate breast area or breast volume. Spatial information based FCM method incorporates neighborhood pixels' intensities into segmentation because neighbored pixels on an image are highly correlated. Kernel based FCM algorithm is developed by transforming pixel intensity using kernel functions to better improve segmentation performance. The proposed segmentation methods are implemented on mammograms and tomosynthetic images and compared with conventional FCM results. Experiment results demonstrate the proposed segmentation methods are much better compared with traditional FCM method, and are more robust to noises. The developed kernel and spatial based FCM method will be applied for differentiation of breast density and abnormal regions within the breast region to examine its performance in reducing false positive segmentations.
KEYWORDS: Breast, 3D modeling, Mammography, Finite element methods, Data modeling, Magnetic resonance imaging, Chemical elements, Computer simulations, 3D acquisition, Tissues
Performing regular mammographic screening and comparing corresponding mammograms taken from multiple
views or at different times are necessary for early detection and treatment evaluation of breast cancer, which is
key to successful treatment. However, mammograms taken at different times are often obtained under different
compression, orientation, or body position. A temporal pair of mammograms may vary significantly due to the
spatial disparities caused by the variety in acquisition environments, including 3D position of the breast, the
amount of pressure applied, etc. Such disparities can be corrected through the process of temporal registration.
We propose to use a 3D finite element model for temporal registration of digital mammography. In this paper,
we apply patient specific 3D breast model constructed from MRI data of the patient, for cases where lesions are
detectable in multiple mammographic views across time. The 3D location of the lesion in the breast model is
computed through a breast deformation simulation step presented in our earlier work. Lesion correspondence
is established by using a nearest neighbor approach in the uncompressed breast volume. Our experiments show
that the use of a 3D finite element model for simulating and analyzing breast deformation contributes to good
accuracy when matching suspicious regions in temporal mammograms.
Breast tomosynthesis is an emerging state-of-the-art three-dimensional (3D) imaging technology that demonstrates
significant early promise in screening and diagnosing breast cancer. However, this kind of image has significant out-of-plane
artifacts due to its limited tomography nature, which affects the image quality and further would interrupt
interpretation. In this paper, we develop a robust deblurring method to remove or suppress blurry artifacts by applying
three-dimensional (3D) nonlinear anisotropic diffusion filtering method. Differential equation of 3D anisotropic
diffusion filtering is discretized using explicit and implicit numerical methods, respectively, combined by first (fixed
grey value) and second (adiabatic) boundary conditions under ten nearest neighbor grids configuration of finite
difference scheme. The discretized diffusion equation is applied in the breast volume reconstructed from the entire
tomosynthetic images of breast. The proposed diffusion filtering method is evaluated qualitatively and quantitatively on
clinical tomosynthesis images. Results indicate that the proposed diffusion filtering method is very powerful in
suppressing the blurry artifacts, and the results also indicate that implicit numerical algorithm with fixed value boundary
condition has better performance in enhancing the contrast of tomosynthesis image, demonstrating the effectiveness of
the proposed filtering method in deblurring the out-of-plane artifacts.
Three-dimensional (3D)-based detection and diagnosis plays important role in significantly improving detection and diagnosis of lung cancers through computed tomography (CT). This paper presents a 3D approach for segmenting and visualizing lung volume by using CT images. An edge-preserving filter (3D sigma filter) is first performed on CT slices to enhance the signal-to-noise ratio, and wavelet transform (WT)-based interpolation incorporated with volume rendering is utilized to construct 3D volume data. Then an adaptive 3D region-growing algorithm is designed to segment lung mask incorporated with automatic seed locating algorithm through fuzzy logic algorithm, in which 3D morphological closing algorithm is performed on the mask to fill out cavities. Finally, a 3D visualization tool is designed to view the volume data, its projections or intersections at any angle. This approach was tested on single detector CT images and the experiment results demonstrate that it is effective and robust. This study lays groundwork for 3D-based computerized detection and diagnosis of lung cancer with CT imaging. In addition, this approach can be integrated into PACS system serving as a visualization tool for radiologists’ reading and interpretation.
Design of classifier in computer-aided diagnosis (CAD) scheme of breast cancer plays important role to its overall performance in sensitivity and specificity. Classification of a detected object as malignant lesion, benign lesion, or normal tissue on mammogram is a typical three-class pattern recognition problem. This paper presents a three-class classification approach by using two-stage classifier combined with support vector machine (SVM) learning algorithm for classification of breast cancer on mammograms. The first classification stage is used to detect abnormal areas and normal breast tissues, and the second stage is for classification of malignant or benign in detected abnormal objects. A series of spatial, morphology and texture features have been extracted on detected objects areas. By using genetic algorithm (GA), different feature groups for different stage classification have been investigated. Computerized free-response receiver operating characteristic (FROC) and receiver operating characteristic (ROC) analyses have been employed in different classification stages. Results have shown that obvious performance improvement in both sensitivity and specificity was observed through proposed classification approach compared with conventional two-class classification approaches, indicating its effectiveness in classification of breast cancer on mammograms.
This paper focuses on evaluating three fuzzy image segmentation algorithms in lung nodule detection scenario: fuzzy entropy-based method, multivariate fuzzy C-means method (MFCM), adaptive fuzzy C-means method (AFCM) and comparing them with the iterative threshold selection method. The experimental result shows that all three methods outperform iterative threshold selection method. The two fuzzy C-means clustering based algorithms achieve better segmentation performance without losing true positives. However, fuzzy entropy-based image segmentation removes the false positives at the cost of losing some true positives, which is a risky approach and hence it is not recommended for lung nodule detection. Moreover, although AFCM outperforms MFCM in true positive detection significantly, in the sense of TPR/FP, MFCM is comparable to AFCM in the confidence interval of significant level 0.95, since AFCM brings in more false positives than MFCM.
Full-field digital mammography (FFDM) as a new breast imaging modality has potential to detect more breast cancers or to detect them at smaller sizes and earlier stages compared with screening film mammography (SFM). However, its performance needs verification, and it would pose new problems for the development of CAD methods for breast cancer detection and diagnosis. Performance evaluation of CAD systems on FFDM and SFM has been conducted in this study, respectively. First, an adaptive CAD system employing a series of advanced modules has been developed on FFDM. Second, a standardization approach has been developed to make the CAD system independent of characteristics of digitizer or imaging modalities for mammography. CAD systems developed previously for SFM and developed in this study for FFDM have been evaluated on FFDM and SFM images without and with standardization, respectively, to examine the performance improvement of the CAD system developed in this study. Computerized free-response receiver operating characteristic (FROC) analysis has been adopted as performance evaluation method. Compared with previous one, the CAD system developed in this study demonstrated significantly performance improvements. However, the comparison results have shown that the performances of final CAD system in this study are not significantly different on FFDM and on SFM after standardization. It needs further study on the assessment of CAD system performance on FFDM and SFM modalities.
A fully optimized computer-aided diagnosis (CAD) system using system-oriented optimization algorithm has been developed for mass detection in digital mammography for the improvement of CAD performance on sensitivity and specificity. Based on a series of developed adaptive modules, simulated annealing (SA) algorithm is employed on CAD system optimization that is a typical combinatorial mixed-discrete optimization problem. The CAD system is optimized on a training database and evaluated through a test database, the cases in both databases are biopsy proven and selected by experienced radiologists. Both optimized and corresponding un-optimized CAD system have been evaluated using the same test database to compare the system performance. Obvious performance improvement has been obtained on optimized system. The results express the effectiveness of the method developed in this paper.
We compared different edge detection algorithms and found that the 'Marr-Hildreth' edge detection have the best performance for macrocalcification shape preservation in our MCCs detection system. Edge detection is one of the most commonly used operations in image analysis. The edges form the outline of the macrocalcifications. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the macrocalcifications can be located and the areas, perimeters, and shapes can be measured. So edge detection is one of the effect methods to preserve the shape of microcalcifications. Based on the edge enhancement method, a new mixed feature multistage method has been developed for improving the false positive (FP) reduction performance. Eleven features were extracted from both spatial and morphology domains in order to describe the micro-calcification clusters (MCCs) from different perspectives. These features are grouped into three categories: gray-level description, shape description and clusters description. This method was combined with neural network used in our false positive reduction, that reduce the false positive from 3.1/image to 0.1/image in 50 full field digital mammograms, The 50 mammograms are with 24 normal images and 26 abnormal images, including 41 microcalcification clusters in our database.
This paper is to evaluate the importance of image preprocessing using multiresolution and multiorientation wavelet transforms on the performance of a previously reported computer assisted diagnostic (CAD) method for breast cancer screening, using digital mammography. An analysis of the influence of WTs on image feature extraction for mass detection is achieved by comparing the discriminate ability of features extracted with and without wavelet based image preprocessing using computed ROC. Three indexes are proposed to assess the segmentation of the mass area with comparison to ground truth. Dat was analyzed on region-of- interest database that included mass and normal regions from digitized mammograms with ground truth. The metrics for measurement of segmentation of the mass clearly demonstrates the importance of image preprocessing methods. Similarly, the relative improvement in performance is observed in feature extraction, where the Az values are increased. The improvement depends on the feature characteristics. The use of methodology in this paper result sin a significant improvement in feature extraction for the previously proposed CAD detection method. We are therefore exploring additional improvement in wavelet based image preprocessing methods, including adaptive methods, to achieve a further improvement in performance on larger image databases.
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