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Biomedical subjects

Maria Kallergi

Publications and source records attributed to Maria Kallergi.

7 recordsLinked to original sources
PubMed2007

Choosing a radiology workstation: technical and clinical considerations.

Choosing a workstation for daily use in the interpretation of digital radiologic images can be a daunting task. There are numerous products available on the market, but differentiating among them and deciding on what is best for a particular environment can be confusing and frustrating. There is no "one-size-fits-all" workstation, so users must consider a variety of factors when choosing a workstation. This review summarizes the critical elements in a radiology workstation and the characteristics one should be aware of and look for in the selection of a workstation. Issues pertaining to both hardware and software aspects of medical workstations, including interface design, are reviewed, particularly as they may affect the interpretation process.

Data Display↗
PubMed2006

Computerized analysis of tissue density effect on missed cancer detection in digital mammography.

This paper presents a study of the analysis of breast density in missed cancer cases and the effect of tissue density on cancer detection. A total of 100 missed cancer cases were collected. The breast density tissue was segmented with a statistical-based method. A set of tests was then applied to examine: (1) the differences in density between the mammograms at the detected stage and that at missed stage; (2) the density difference between the cancerous mammograms and their contra-lateral normal mammograms in the missed cancer cases; (3) the effect of breast density on CAD cancer detection. The results demonstrate that breast density is an important factor affecting not only radiologist's reading but also CAD performance. In order to improve early detection of breast cancer, a special effort should be directed to the high dense breast cases in CAD system design.

Breast Neoplasms↗
PubMed2006

High-performance wavelet compression for mammography: localization response operating characteristic evaluation.

PURPOSE: To evaluate the accuracy of a visually lossless, image-adaptive, wavelet-based compression method for achievement of high compression rates at mammography. MATERIALS AND METHODS: The study was approved by the institutional review board of the University of South Florida as a research study with existing medical records and was exempt from individual patient consent requirements. Patient identifiers were obliterated from all images. The study was HIPAA compliant. An algorithm based on scale-specific quantization of biorthogonal wavelet coefficients was developed for the compression of digitized mammograms with high spatial and dynamic resolution. The method was applied to 500 normal and abnormal mammograms from 278 patients who were 32-85 years old, 85 of whom had biopsy-proved cancer. Film images were digitized with a charge-coupled device-based digitizer. The original and compressed reconstructed images were evaluated in a localization response operating characteristic experiment involving three radiologists with 2-10 years of experience in reading mammograms. RESULTS: Compression rates in the range of 14:1 to 2051:1 were achieved, and the rates were dependent on the degree of parenchymal density and the type of breast structure. Ranges of the area under the receiver operating characteristic curve were 0.70-0.83 and 0.72-0.86 for original and compressed reconstructed mammograms, respectively. Ranges of the area under the localization response operating characteristic curve were 0.39-0.65 and 0.43-0.71 for original and compressed reconstructed mammograms, respectively. The localization accuracy increased an average of 6% (0.04 of 0.67) with the compressed mammograms. Localization performance differences were statistically significant with P = .05 and favored interpretation with the wavelet-compressed reconstructed images. CONCLUSION: The tested wavelet-based compression method proved to be an accurate approach for digitized mammography and yielded visually lossless high-rate compression and improved tumor localization.

Adult↗
PubMed2004

Clinical evaluation of a new digitizing device for improved film mammography.

RATIONALE AND OBJECTIVES: The study was designed to evaluate a new digitizing device, the iView (Maxxvision, LLC, Gainesville, FL), which aims to replace the magnifying glass in mammography with real-time film digitization, display, and processing. MATERIALS AND METHODS: A receiver operating characteristic (ROC) experiment was performed with 5 certified mammographers and 114 mammograms that were read with and without the iView. A satisfaction survey was also conducted on the system's features and usefulness. RESULTS: Data analysis suggested that (1) Cancer sensitivity could improve with the use of the iView system. ROC area differences showed improvements from 2% to 24% although these were not always statistically significant. At a false positive rate of 0.2, the true positive rate increased up to 60% depending on the set of cases and the observer's experience. (2) Specificity could also be improved. At a true positive rate of 0.9, the false positive rate decreased by as much as 55%. (3) Most observers felt more confident in their decisions when using the iView, although the prototype's ergonomic problems did not allow full utilization of its capabilities. CONCLUSION: Our pilot clinical study showed that the iView has the potential to improve mammogram interpretation. In addition, the system could broaden the applicability of electronic information and provide wider access to digital technology through a relatively simple and cost-effective approach. Observers recommended several improvements in the ergonomics and default display of the system that are currently implemented by the company. A larger clinical study of the improved system is necessary to clearly demonstrate its clinical value for mammography.

Breast Neoplasms↗
PubMed2004

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 patient's 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.

Algorithms↗
PubMed2004

Improved interpretation of digitized mammography with wavelet processing: a localization response operating characteristic study.

OBJECTIVE: Our objective was the implementation and evaluation of a novel enhancement technique for improved interpretation of high-resolution digitized mammograms from computer monitors. MATERIALS AND METHODS: A wavelet algorithm was designed to attenuate the image spectral characteristics responsible for the long-range image correlation that often interferes with digital display. The algorithm was evaluated with a localization response operating characteristic (LROC) experiment with 500 negative, benign, and cancer cases with masses and calcification clusters. Three observers reviewed the original and wavelet-enhanced images on a 5-Mpixel monitor using a custom-made workstation user interface. RESULTS: Performance indexes were estimated for four different case combinations, each observer, and each interpretation mode. Wavelet enhancement improved the performance of all observers in all case combinations. Detection accuracy ranged from 0.678 to 0.827 for the unprocessed original data and 0.709-0.871 for the enhanced cases. Localization accuracy ranged from 0.547 to 0.785 for the original images and 0.568-0.847 for the enhanced cases, yielding increases of 5-15%. The difference between enhanced and original performances was statistically significant at the 0.10 level and in a few combinations at the 0.05 level. CONCLUSION: Soft-copy digitized mammography could replace standard film mammography under appropriate display parameters and conditions. The optimization of the soft-copy quality is expected to require more advanced processing techniques than standard gray-scale adjustments. Wavelet-based algorithms, such as the one proposed here, offer better soft-copy quality than the originals and a better starting point for additional manual gray-scale adjustments or automated postprocessing.

Algorithms↗