Biomedical subjects

P Sakellaropoulos

Publications and source records attributed to P Sakellaropoulos.

9 recordsLinked to original sources

PubMed2003

A wavelet-based spatially adaptive method for mammographic contrast enhancement.

A method aimed at minimizing image noise while optimizing contrast of image features is presented. The method is generic and it is based on local modification of multiscale gradient magnitude values provided by the redundant dyadic wavelet transform. Denoising is accomplished by a spatially adaptive thresholding strategy, taking into account local signal and noise standard deviation. Noise standard deviation is estimated from the background of the mammogram. Contrast enhancement is accomplished by applying a local linear mapping operator on denoised wavelet magnitude values. The operator normalizes local gradient magnitude maxima to the global maximum of the first scale magnitude subimage. Coefficient mapping is controlled by a local gain limit parameter. The processed image is derived by reconstruction from the modified wavelet coefficients. The method is demonstrated with a simulated image with added Gaussian noise, while an initial quantitative performance evaluation using 22 images from the DDSM database was performed. Enhancement was applied globally to each mammogram, using the same local gain limit value. Quantitative contrast and noise metrics were used to evaluate the quality of processed image regions containing verified lesions. Results suggest that the method offers significantly improved performance over conventional and previously reported global wavelet contrast enhancement methods. The average contrast improvement, noise amplification and contrast-to-noise ratio improvement indices were measured as 9.04, 4.86 and 3.04, respectively. In addition, in a pilot preference study, the proposed method demonstrated the highest ranking, among the methods compared. The method was implemented in C++ and integrated into a medical image visualization tool.

Algorithms↗

PubMed2001

Quantifying image quality at breast periphery vs mammary gland in mammography using wavelet analysis.

Use of high contrast film-screen systems in mammography, in combination with the fact that exposure parameters are selected to ensure good visualization of the mammary gland, results in overexposure of the film area corresponding to the breast periphery, therefore decreasing image quality. The aim of this work was to provide a quantitative evaluation of image quality at the breast periphery compared with the mammary gland. To deal with the difficulties in quantification of image quality introduced by low contrast encountered at the breast periphery, wavelet analysis has been used for derivation of a contrast indicator (CI) and a noise indicator (NI), taking into account local grey level variations. Gradient magnitude coefficients corresponding to region of interest (ROI) grey level values are the basis of CI definition. Mammary gland and breast periphery were sampled by equally spaced ROIs, the quantity of which was determined by a heuristic method. For NI definition, the power values of gradient magnitude coefficients corresponding to the ROI were utilized. Image quality at the breast periphery compared with the mammary gland was evaluated using 150 craniocaudal images from the Digital Database for Screening Mammography. Measurements were carried out using a tool developed in our department. A 50% contrast decrease at the breast periphery was observed, while noise decreased by approximately 2%.

Breast Diseases↗

PubMed2000

A digital density equalization technique to improve visualization of breast periphery in mammography.

In mammographic imaging, the film area corresponding to the breast periphery is overexposed, resulting in high optical density and degraded contrast in this region. A digital, model-driven density equalization technique was designed and developed to overcome this overexposure problem, taking into account the non-linear characteristic curve of the film-digitizer system. The method is based on several image processing and analysis techniques, such as thresholding, which is used to segment the pixels of the mammogram belonging to the breast region from the background, and wavelet-based fusion, which is used to equalize the pixels of breast periphery selectively while leaving the remaining breast region unaffected. Initial application of the method resulted in density-equalized mammographic images, characterized by improved contrast at the breast periphery.

Breast Neoplasms↗

PubMed1998

Distance learning in mammographic digital image processing.

The potential of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning system in medical imaging. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as World Wide Web (WWW) sites. Sites are implemented by integration and customization of available components. The system evolves around network-delivered interactive multimedia courses and network-based tutoring, which constitute its main learning features. This potential has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied on mammograms. Emphasis is given in the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high speed access can be achieved via LAN or local ISDN connections.

Biomedical Engineering↗

PubMed1997

PRONET services for distance learning in mammographic image processing.

The potential of telematics services is investigated with respect to learning needs of medical physicists and biomedical engineers. Telematics services are integrated into a system, the PRONET, which evolves around multimedia computer based courses and distance tutoring support. In addition, information database access and special interest group support are offered. System architecture is based on a component integration approach. The services are delivered in three modes: LAN, ISDN and Internet. Mammographic image processing is selected as an example content area.

Biomedical Engineering↗

PubMed1997

Use of wavelet analysis for contrast enhancement in mammography.

A wavelet analysis approach to contrast enhancement in mammography is presented. The approach consists of application of non-linear enhancement of multiscale gradient magnitudes utilising edge location information. The performance of wavelet analysis is evaluated by means of a breast phantom. Preliminary results indicate the value of the approach in contrast enhancement and edge preservation for both simulated tumours and microcalcifications.

Breast Neoplasms↗

PubMedDate not supplied

Using component technologies for web based wavelet enhanced mammographic image visualization.

The poor contrast detectability of mammography can be dealt with by domain specific software visualization tools. Remote desktop client access and time performance limitations of a previously reported visualization tool are addressed, aiming at more efficient visualization of mammographic image resources existing in web or PACS image servers. This effort is also motivated by the fact that at present, web browsers do not support domain-specific medical image visualization. To deal with desktop client access the tool was redesigned by exploring component technologies, enabling the integration of stand alone domain specific mammographic image functionality in a web browsing environment (web adaptation). The integration method is based on ActiveX Document Server technology. ActiveX Document is a part of Object Linking and Embedding (OLE) extensible systems object technology, offering new services in existing applications. The standard DICOM 3.0 part 10 compatible image-format specification Papyrus 3.0 is supported, in addition to standard digitization formats such as TIFF. The visualization functionality of the tool has been enhanced by including a fast wavelet transform implementation, which allows for real time wavelet based contrast enhancement and denoising operations. Initial use of the tool with mammograms of various breast structures demonstrated its potential in improving visualization of diagnostic mammographic features. Web adaptation and real time wavelet processing enhance the potential of the previously reported tool in remote diagnosis and education in mammography.

Female↗

PubMedDate not supplied

An image visualization tool in mammography.

The poor detectability of diagnostic mammographic features, due to their low contrast, is dealt with by a software visualization tool. The tool is domain specific to medical imaging and consequently mammographic imaging, and it is envisaged as part of medical image visualization and manipulation stations. Domain specificity is served by the tool conformance to DICOM 3.0 part 10 image format specifications, specifically PAPYRUS 3.0, window width/level display adjustments of image dynamic range of up to 16 bits, and application of visualization operations to user-defined regions of interest in addition to global operations. The software has been designed and implemented according to an object oriented approach in Visual C++. The tool user interface is friendly, based on a widely used windowing paradigm, the Microsoft Foundation Class library version 4.2, which provides interface items, such as windows, dialogue boxes, lists, slide bars, buttons, etc. The visualization functionality offered by the tool relies on the following three categories of image processing algorithms: dynamic range adjustments by pixel intensity transformations, contrast enhancement and noise suppression by spatial domain direct manipulation of image pixels or by manipulation of wavelet coefficients. The first two categories of algorithms are implemented in real time. Initial use of the tool has demonstrated its potential in improving the detectability of diagnostic mammographic image features.

Algorithms↗

PubMedDate not supplied

A network-based training environment: a medical image processing paradigm.

The capability of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning environment. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as WWW sites. Sites are implemented by customization of available components. The environment integrates network-delivered interactive multimedia courses, network-based tutoring, SIG support, information databases of professional interest, as well as course and tutoring management. This capability has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied to mammograms. Emphasis is given to the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high-speed access can be achieved via LAN or local ISDN connections. Network based training offers new means of improved access and sharing of learning resources and expertise, as promising supplements in training.

Computer Communication Networks↗