Biomedical subjects

M Kallergi

Publications and source records attributed to M Kallergi.

6 recordsLinked to original sources

PubMed1996

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.

Breast Diseases↗

PubMed1995

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.

Calcinosis↗

PubMed1994

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.

Algorithms↗

PubMed1994

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.

Breast Neoplasms↗

PubMed1994

Wavelet compression and segmentation of digital mammograms.

An initial evaluation of Haar wavelets is presented in this study for the compression of mammographic images. Fifteen mammograms with 105 microns/pixel resolution and varying dynamic range (10 and 12 bits per pixel) containing clustered microcalcifications were compressed with two different rates. The quality and content of the compressed reconstructed images was evaluated by an expert mammographer. The visualization of the cluster was on the average good but degraded with increasing compression because of the discontinuities introduced by these types of wavelets as the compression rate increases. However, the artifacts in the decoded images were seen as totally artificial and were not misinterpreted by the radiologist as calcifications. The classification of the parenchymal densities did not change significantly but the morphology of the calcifications was increasingly distorted as the compression rate increased leading to lower estimates of the suspiciousness of the cluster and higher uncertainties in the diagnosis. The uncompressed and two sets of compressed images were also processed by a wavelet method to extract the calcifications. Despite the fact that the segmentation algorithm generated several false-positive signals in highly compressed images, all true clusters were successfully segmented indicating that the compression process preserved the features of interest. Our preliminary results indicated that wavelets could be used to achieve high compression rates of mammographic images without losing small details such as microcalcification clusters as well as detect the calcifications from either the uncompressed or compressed reconstructed data. Further research and application of multiresolution analysis to digital mammography is continuing.

Algorithms↗

PubMedDate not supplied

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.

Algorithms↗