Content-based retrieval for the comparative analysis of mammograms containing masses is presented as one part of a larger project on the development of a content-based image retrieval system for computer-aided diagnosis of breast cancer. In response to a query, masses characterized by objectively determined values related to specific mammographic features are retrieved from a database. The retrieved mammograms and their associated patient information may be used to support the radiologist's decision-making process when examining difficult-to-diagnose cases. We investigate the use of objective measures of shape, edge sharpness, and texture to retrieve mammograms with similar masses. Experiments were conducted with 57 regions (20 malignant and 37 benign) in mammograms containing masses. Three shape factors representing compactness, fractional concavity (Fcc), and spiculation index; Haralick's 14 statistical texture features; and four edge-sharpness measures were computed for use as indices for each of the mass regions. The feature values were evaluated with linear discriminant analysis, logistic regression, and Mahalanobis distance for their effectiveness in classifying the masses as benign or malignant. The three most effective features of Fcc, acutance (A), and sum entropy (F8) were selected from the 21 computed features based on the area under the receiver operating characteristics curve and logistic regression. Linear discriminant analysis with Fcc resulted in the highest sensitivity of 100% and specificity of 97%. The texture feature F8 and acutance A yielded average accuracies of 61% and 74%, respectively. A measure of retrieval accuracy known as precision was determined to be 91% when using the three selected features. However, the shape measure of fractional concavity on its own yielded a precision rate of 95%. The methods proposed should lead to an efficient tool for computer-aided diagnosis of breast cancer.
Screening mammography is the most efficient and cost-effective method available for detecting the signs of early breast cancer in asymptomatic women between the ages of 50 and 69. To improve the detection rate and reduce the number of unnecessary biopsies, many different computer-aided diagnosis techniques have been developed. Many of these techniques use image processing algorithms to automatically segment and classify the images. The decision-making process associated with the evaluation of mammograms is complex and incorporates multiple sources of information from standard medical knowledge and radiology to pathology. The use of this information combined with the results of image processing offers new challenges to the field of data and information fusion. In this paper, we describe the different information sources and their data as well as the framework that is needed to support this type of fusion. A database of breast cancer screening cases forms the basis of the resulting fusion model. The database and decision-level fusion techniques will facilitate unique and specialized approaches for efficient and sophisticated diagnosis of breast cancer.
A multi-tolerance region-growing algorithm for automatically detecting and circumscribing calcifications in digitized mammographic images was developed. Independent studies comparing various segmentation methods showed that the multi-tolerance technique works well. However, the method is computationally expensive due to the checking of the validity of the grown region at every tolerance level until the optimal region is obtained for each calcification. Furthermore, a single mammogram may contain as many as a few hundred calcifications. In order to reduce processing time, the calcification detection algorithm was implemented on a cluster of processors using the message passing interface. In the parallel implementation, the master processor partitions the image via histogram thresholding, and sends seed pixels to the slaves to execute the multi-tolerance region-growing procedure. The slave processors grow regions, calculating a few shape parameters at each tolerance level. The parameters are used to compute distance measures which are compared until the minimum change in distance is achieved. Shape factors are then computed to describe the roughness of each region's final boundary and returned to the master processor. Initial trails have shown a speedup factor of three to eight when comparing the use of 13 slave processors to the use of one slave processor.
An adaptive neighborhood contrast enhancement (ANCE) technique was developed to improve the perceptibility of features in digitized mammographic images for use in breast cancer screening. The computationally intensive algorithm was implemented on a cluster of 30 DEC Alpha processors using the message passing interface. The parallel implementation of the ANCE technique utilizes histogram- based image partitioning with each partition consisting of pixels of the same gray-level value regardless of their location in the image. The master processor allots one set of pixels to each slave processor. The slave returns the results to the master, and the master than sends a new set of pixels to the slave for processing. This procedure continues until there are no sets of pixels left. The subdivision of the original image based on gray-level values guarantees that slave processors do not process the same pixel, and is specifically well-suited to the characteristics of the ANCE algorithm. The parallelism value of the problem is approximately 16, i.e., the performance does not improve significantly when more than 16 processors are used. The result is a substantial improvement in processing time, leading to the enhancement of 4 K X 4 K pixel images in the range of 20 to 60 seconds.
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