Bi-plane correlation imaging (BCI) is a new imaging approach that utilizes angular information from a bi-plane digital acquisition in conjunction with computer assisted detection (CAD) to reduce the degrading influence of anatomical noise in the detection of subtle lesions in planar images. An anthropomorphic chest phantom, supplemented with added nodule phantoms (5-13 mm at the image plane), was imaged from different posterior projections within a ±12° range by moving the x-ray tube vertically and horizontally with respect to the detector. Each image was analyzed using a basic front-end single-view CAD algorithm. The correlation of the suspect lesions from the PA view with those from each of the oblique views was examined using a priori knowledge of the acquisition geometry. The correlated suspect lesions were registered as positive. Using an optimum --3° vertical geometry and processing parameters, BCI resulted in 62.5% sensitivity, 1.5 FP/image, and 0.885 PPV. The corresponding values from the observer experiment were 56% sensitivity, 10.8 FP/image, and 0.45 PPV, respectively. Compared to single-view CAD results, the BCI reduced sensitivity by 20%. However, the corresponding reduction in FPs was notably higher (94%) leading to 140% improvement in the PPV. Changes in processing parameters could result in higher PPV and lower FP/image at the expense of lower sensitivity. Similar findings were indicated for small (5-9 mm) and large (9-13 mm) nodules, but the relative improvement was significantly higher for smaller nodules. (The research was supported by a grant from the NIH, R21CA91806.)
A computer algorithm for fast identification and localization of structures of interest in images is presented. The algorithm is based on the analysis of a reduced set of image neighborhoods selected randomly by a constrained sampling of an associated image map of much smaller spatial resolution. The general approach is demonstrated by estimating the relative location of the breast tissue on a dataset of 860 digitized mammographic images. The computational times and breast tissue localization error rates are reported for different reduced spatial resolution image maps and three different features used for the corresponding neighborhood analysis. Our results show significant improvement on the error rates and computational times obtained with our approach compared to a pixel intensity thresholding approach. The algorithm implementation is very simple, requires less computation time than the sequential processing of each one of the image elements in a raster pattern and can be easily included into a hierarchical image analysis model.
The purpose of the study was to develop and evaluate a content-based image retrieval (CBIR) approach as a computer aid for the detection of masses in screening mammograms. The study was based on the Digital Database for Screening Mammography (DDSM). Initially, a knowledge database of 1,009 mammographic regions was created. They were all 512x512 pixel ROIs with known pathology. Specifically, there were 340 ROIs depicting a biopsy-proven malignant mass, 341 ROIs with a benign mass, and the remaining 328 ROIs were normal. Subsequently, the CBIR algorithm was implemented using mutual information (MI) as the similarity metric for image retrieval. The CBIR algorithm formed the basis of a knowledge-based CAD system. The system operated as follows. Given a databank of mammographic regions with known pathology, a query suspicious mammographic region was evaluated. Based on their information content, all similar cases in the databank were retrieved. The matches were rank-ordered and a decision index was calculated using the query's best matches. Based on a leave-one out sampling scheme, the CBIR-CAD system achieved an ROC area index Az= 0.87±0.01 and a partial ROC area index 0.90Az = 0.45±0.03 for the detection of masses in screening mammograms.
A hierarchical Markov random field (MRF) model for mammographic structure segmentation using multiple spatial and intensity image resolutions is proposed. The general image model is formed by a sequence of representations at different spatial and intensity scales. Through the hierarchical structure of the MRF model, components at different local spatial resolutions are used to condition the corresponding intensity resolution and the spatial distribution of the intensity components. As a first step, only breast skin edge and non fat breast parenchyma (Cooper's ligaments, blood vessels and fibroglandular tissue) have been included into the model and implemented. Three basic priors for the local spatial intensity distribution (texture) are defined. An iterated conditional mode (ICM) optimization procedure is implemented, the lower resolution representations are used sequentially to form the initial image configurations for the ICM procedure. The proposed approach was tested using 100 digitized mammograms (at a resolution of 100 microns and 12 bits per pixel). The mammograms are from three different views and different breast parenchyma densities. Results for breast skin edge and breast parenchyma were obtained and evaluated visually. For all cases, the location of the three possible structures (skin, parenchyma and background) was identified correctly.
An approach for the classification of normal or abnormal lung parenchyma from selected regions of interest (ROIs) of chest radiographs is presented for computer aided diagnosis of interstitial lung disease (ILD). The proposed approach uses a feed-forward neural network to classify each ROI based on a set of isotropic texture measures obtained from the joint grey level distribution of pairs of pixels separated by a specific distance. Two hundred ROIs, each 64 X 64 pixels in size (11 X 11 mm), were extracted from digitized chest radiographs for testing. Diagnosis performance was evaluated with the leave-one-out method. Classification of independent ROIs achieved a sensitivity of 90% and a specificity of 84% with an area under the receiver operating characteristic curve of 0.85. The diagnosis for each patient was correct for all cases when a `majority vote' criterion for the classification of the corresponding ROIs was applied to issue a normal or ILD patient classification. The proposed approach is a simple, fast, and consistent method for computer aided diagnosis of ILD with a very good performance. Further research will include additional cases, including differential diagnosis among ILD manifestations.
A hierarchical Markov random field (MRF) modeling approach is presented for the classification of textures in selected regions of interest (ROIs) of chest radiographs. The procedure integrates possible texture classes and their spatial definition with other components present in an image such as noise and background trend. Classification is performed as a maximum a-posteriori (MAP) estimation of texture class and involves an iterative Gibbs- sampling technique. Two cases are studied: classification of lung parenchyma versus bone and classification of normal lung parenchyma versus miliary tuberculosis (MTB). Accurate classification was obtained for all examined cases showing the potential of the proposed modeling approach for texture analysis of radiographic images.
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