Maria Kallergi

Tree-structured nonlinear filters in digital mammography

A new class of nonlinear filters with more robust characteristics for noise suppression and detail preservation is proposed for processing digital mammographic images. The new algorithm consists of two major filtering blocks: (a) a multistage tree-structured filter for image enhancement that uses central weighted median filters as basic sub-filtering blocks and (b) a dispersion edge detector. The design of the algorithm also included the use of linear and curved windows to determine whether variable shape windowing could improve detail preservation. First, the noise-suppressing properties of the tree-structured filter were compared to single filters, namely the median and the central weighted median with conventional square and variable shape adaptive windows; simulated images were used for this purpose. Second, the edge detection properties of the tree-structured filter cascaded with the dispersion edge detector were compared to the performance of the dispersion edge detector alone, the Sobel operator, and the single median filter cascaded with the dispersion edge detector. Selected mammographic images with representative biopsy-proven malignancies were processed with all methods and the results were visually evaluated by an expert mammographer. In all applications, the proposed filter suggested better detail preservation, noise suppression, and edge detection than all other approaches and it may prove to be a useful tool for computer-assisted diagnosis in digital mammography.

Computer aided diagnosis of mammographic microcalcification clusters

Computer-aided diagnosis techniques in medical imaging are developed for the automated differentiation between benign and malignant lesions and go beyond computer-aided detection by providing cancer likelihood for a detected lesion given image and/or patient characteristics. The goal of this study was the development and evaluation of a computer-aided detection and diagnosis algorithm for mammographic calcification clusters. The emphasis was on the diagnostic component, although the algorithm included automated detection, segmentation, and classification steps based on wavelet filters and artificial neural networks. Classification features were selected primarily from descriptors of the morphology of the individual calcifications and the distribution of the cluster. Thirteen such descriptors were selected and, combined with patients age, were given as inputs to the network. The features were ranked and evaluated for the classification of 100 high-resolution, digitized mammograms containing biopsy-proven, benign and malignant calcification clusters. The classification performance of the algorithm reached a 100% sensitivity for a specificity of 85% (receiver operating characteristic area index Az = 0.98 +/- 0.01). Tests of the algorithm under various conditions showed that the selected features were robust morphological and distributional descriptors, relatively insensitive to segmentation and detection errors such as false positive signals. The algorithm could exceed the performance of a similar visual analysis system that was used as basis for development and, combined with a simple image standardization process, could be applied to images from different imaging systems and film digitizers with similar sensitivity and specificity rates.

Evaluating the performance of detection algorithms in digital mammography.

The initial and relative evaluation of computer methodologies developed for assisting diagnosis in mammography is usually done by comparing the computer output to ground truth data provided by experts and/or biopsy. Reported studies, however, give little information on how the performance indices of computer assisted diagnosis (CAD) algorithms are determined in this initial stage of evaluation. Several strategies exist in the estimation of the true positive (TP) and false positive (FP) rates with respect to ground truth. Adopting one strategy over another yields different performance rates that can be over- or underestimates of the true performance. Furthermore, the estimation of pairs of TP and FP rates gives a partial picture of the performance of an algorithm. It is shown in this work that new performance indices are needed to fully describe the degree of detection (part or whole) and the type of detection (single calcification, cluster of calcifications, mass, or artifact). Several evaluation strategies were tested. The one that yielded the most realistic performances included the following criteria: The detected area should be at least 50% of the true area and no more than four times the true area in order to be considered TP. At least three true calcifications should be detected to within 1 cm2 with nearest neighbor distances of less than square root(2) cm for a cluster to be considered TP. Separate detection measures should be established and used for artifacts and naturally occurring structures to maximize the benefits of the evaluation. Finally, it is critical that CAD investigators provide information on the tested image set as well as the criteria used for the evaluation of the algorithms to allow comparisons and better understanding of their methodologies.

Tree-structured non-linear filter and wavelet transform for microcalcification segmentation in digital mammography

A novel algorithm was developed for computer aided diagnosis of microcalcification clusters in digital mammography. The method includes: (a) tree-structured central weighted median filters with variable shape windowing to suppress image noise but preserve image details; (b) a quasi range dispersion edge detector to increase edge contrast and definition; and (c) tree-structured wavelets for calcification segmentation. The preliminary evaluation of the method on nine mammograms showed that 100% sensitivity can be achieved at the expense of four false positive clusters per image. Research is ongoing for further optimization of the algorithm to reduce the number of false alarms and establish its clinical value.

Tree structured wavelet transform segmentation of microcalcifications in digital mammography

A novel multistage algorithm is proposed for the automatic segmentation of microcalcification clusters (MCCs) in digital mammography. First, a previously reported tree structured nonlinear filter is proposed for suppressing image noise, while preserving image details, to potentially reduce the false positive (FP) detection rate for MCCs. Second, a tree structured wavelet transform (TSWT) is applied to the images for microcalcification segmentation. The TSWT employs quadrature mirror filters as basic subunits for both multiresolution decomposition and reconstruction processes, where selective reconstruction of subimages is used to segment MCCs. Third, automatic linear scaling is then used to display the image of the segmented MCCs on a computer monitor for interpretation. The proposed algorithms were applied to an image database of 100 single view mammograms at a resolution of 105 microns and 12 bits deep (4096 gray levels). The database contained 50 cases of biopsy proven malignant MCCs, 8 benign cases, and 42 normal cases. The measured sensitivity (true positive detection rate) was 94% with a low FP detection rate of 1.6 MCCs/image. The image details of the segmented MCCs were reasonably well preserved, for microcalcification of less than 500 microns, with good delineation of the extent of the microcalcification clusters for each case based on visual criteria.

Computer assisted diagnosis for digital mammography

This article describes the application of wavelet transform for image enhancement in medical imaging. The initial clinical application is the enhancement of microcalcification clusters (MCCs) in digitized mammograms to improve both their visualization and their detection using computer assisted diagnostic (CAD) methods. The potential universal application for improved visual interpretation of medical images using a computer monitor is also demonstrated. The early detection of MCCs is important in screening programs since their presence is often associated with a high incidence of breast cancer. The enhancement of MCCs is an excellent model for real world evaluation of the wavelet transform. The detection of MCCs presents a significant challenge to the performance characteristics of X-ray imaging sensors and image display monitors since microcalcifications vary in size, shape, signal intensity, and contrast and may be located in areas of very dense parenchymal tissue, making their detection difficult. The classification of MCCs, in turn, as benign or malignant, requires their morphology and detail to be preserved. >

The application of fractal analysis to mammographic tissue classification

As a first step in determining the efficacy of using computers to assist in diagnosis of medical images, an investigation has been conducted which utilizes the patterns, or textures, in the images. To be of value, any computer scheme must be able to recognize and differentiate the various patterns. An obvious example of this in mammography is the recognition of tumorous tissue and non-malignant abnormal tissue from normal parenchymal tissue. We have developed a pattern recognition technique which uses features derived from the fractal nature of the image. Further, we are able to develop mathematical models which can be used to differentiate and classify the many tissue types. Based on a limited number of cases of digitized mammograms, our computer algorithms have been able to distinguish tumorous from healthy tissue and to distinguish among various parenchymal tissue patterns. These preliminary results indicate that discrimination based on the fractal nature of images may well represent a viable approach to utilizing computers to assist in diagnosis.

Improved method for automatic identification of lung regions on chest radiographs.

Abstract Rationale and Objectives The authors performed this study to evaluate an algorithm developed to help identify lungs on chest radiographs. Materials and Methods Forty clinical posteroanterior chest radiographs obtained in adult patients were digitized to 12-bit gray-scale resolution. In the proposed algorithm, the authors simplified the current approach of edge detection with derivatives by using only the first derivative of the horizontal and/or vertical image profiles. In addition to the derivative method, pattern classification and image feature analysis were used to determine the region of interest and lung boundaries. Instead of using the traditional curve-fitting method to delineate the lung, the authors applied an iterative contour-smoothing algorithm to each of the four detected boundary segments (costal, mediastinal, lung apex, and hemidiaphragm edges) to form a smooth lung boundary. Results The algorithm had an average accuracy of 96.0% for the right lung and 95.2% for the left lung and was especially useful in the delineation of hemidiaphragm edges. In addition, it took about 0.775 second per image to identify the lung boundaries, which is much faster than that of other algorithms noted in the literature. Conclusion The computer-generated segmentation results can be used directly in the detection and compensation of rib structures and in lung nodule detection.

Image segmentation in digital mammography: comparison of local thresholding and region growing algorithms.

Local thresholding and region-growing algorithms are developed and applied to digitized mammograms to quantify the parenchymal densities. The algorithms are first evaluated and optimized on phantom images reflecting varying image contrast, X-ray exposure conditions, and time-related changes. The difference between the segmentation results of the two techniques is less than 6% on the phantom images and 11% on the mammograms. The agreement between the computerized procedures and a manual one is in the range of 74-98%, depending on the breast parenchymal pattern and segmentation algorithm. The results show that computerized parenchymal classification of digitized mammograms is possible and independent of exposure.

Interpretation of calcifications in screen/film, digitized, and wavelet-enhanced monitor-displayed mammograms: a receiver operating characteristic study.

RATIONALE AND OBJECTIVES The acceptance of filmless digital mammography is currently limited by digitization and display drawbacks, as well as bias toward hard-copy interpretation. In the current study, we evaluated a wavelet-based image enhancement method for the filmless interpretation of breast calcifications. METHODS A set of 100 mammograms (58 with calcification clusters) was digitized at 105 microns and 4,096 gray levels per pixel and was processed with nonlinear filters and wavelets. Standard receiver operating characteristic analysis was performed by four radiologists, who independently read the films, the unprocessed digital images, and unprocessed and wavelet-enhanced digital images presented simultaneously. RESULTS Statistical differences were observed between screen/film and unprocessed digitized mammography displayed on monitors. Differences were not significant when wavelet enhancement was included in the monitor display. Interobserver variation in the digitized reading was greater than in film reading, but the wavelet enhancement reduced the difference. CONCLUSION Wavelet-enhanced digital mammograms may assist radiologists in diagnosing calcifications directly from computer monitors and may compensate for current technologic limitations. A study with a larger data-base is needed before this method is accepted for clinical use.