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.

Diagnosis of microcalcifications (MCs) is challenged by the presence of dense breast parenchyma, resulting in low specificity values and thus in unnecessary biopsies. The current study investigates whether texture properties of the tissue surrounding MCs can contribute to breast cancer diagnosis. A case sample of 100 biopsy-proved MC clusters (46 benign, 54 malignant) from 85 dense mammographic images, included in the Digital Database for Screening Mammography, was analysed. Regions of interest (ROIs) containing the MCs were pre-processed using a wavelet-based contrast enhancement method, followed by local thresholding to segment MCs; the segmented MCs were excluded from original image ROIs, and the remaining area (surrounding tissue) was subjected to texture analysis. Four categories of textural features (first order statistics, co-occurrence matrices features, run length matrices features and Laws' texture energy measures) were extracted from the surrounding tissue. The ability of each feature category in discriminating malignant from benign tissue was investigated using a k-nearest neighbour (kNN) classifier. An additional classification scheme was performed by combining classification outputs of three textural feature categories (the most discriminating ones) with a majority voting rule. Receiver operating characteristic (ROC) analysis was conducted for classifier performance evaluation of the individual textural feature categories and of the combined classification scheme. The best performance was achieved by the combined classification scheme yielding an area under the ROC curve (A(z)) of 0.96 (sensitivity 94.4%, specificity 80.0%). Texture analysis of tissue surrounding MCs shows promising results in computer-aided diagnosis of breast cancer and may contribute to the reduction of unnecessary biopsies.

The first step in lung analysis by CT is the identification of the lung border. To deal with the increased number of sections per scan in thin-slice multidetector CT, it has been crucial to develop accurate and automated lung segmentation algorithms. In this study, an automated method for lung segmentation of thin-slice CT data is presented. The method exploits the advantages of a two-dimensional wavelet edge-highlighting step in lung border delineation. Lung volume segmentation is achieved with three-dimensional (3D) grey level thresholding, using a minimum error technique. 3D thresholding, combined with the wavelet pre-processing step, successfully deals with lung border segmentation challenges, such as anterior or posterior junction lines and juxtapleural nodules. Finally, to deal with mediastinum border under-segmentation, 3D morphological closing with a spherical structural element is applied. The performance of the proposed method is quantitatively assessed on a dataset originating from the Lung Imaging Database Consortium (LIDC) by comparing automatically derived borders with the manually traced ones. Segmentation performance, averaged over left and right lung volumes, for lung volume overlap is 0.983+/-0.008, whereas for shape differentiation in terms of mean distance it is 0.770+/-0.251 mm (root mean square distance is 0.520+/-0.008 mm; maximum distance is 3.327+/-1.637 mm). The effect of the wavelet pre-processing step was assessed by comparing the proposed method with the 3D thresholding technique (applied on original volume data). This yielded statistically significant differences for all segmentation metrics (p<0.01). Results demonstrate an accurate method that could be used as a first step in computer lung analysis by CT.

No abstract

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%.

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.

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.

Lung segmentation is a necessary first step to computer analysis in lung CT. It is crucial to develop automated segmentation algorithms capable of dealing with the amount of data produced in thin slice multidetector CT and also to produce accurate border delineation in cases of high density pathologies affecting the lung border. In this study an automated method for lung segmentation of thin slice CT data is proposed. The method exploits the advantage of a wavelet preprocessing step in combination with the minimum error thresholding technique applied on volume histogram. Performance averaged over left and right lung volumes is in terms of: lung volume overlap 0.983 ± 0.008, mean distance 0.770 ± 0.251 mm, rms distance 0.520 ± 0.008 mm and maximum distance differentiation 3.327 ± 1.637 mm. Results demonstrate an accurate method that could be used as a first step in computer lung analysis in CT.