Biomedical subjects

R Highnam

Publications and source records attributed to R Highnam.

6 recordsLinked to original sources

PubMed2006

Breast composition measurements using retrospective standard mammogram form (SMF).

The standard mammogram form (SMF) representation of an x-ray mammogram is a standardized, quantitative representation of the breast from which the volume of non-fat tissue and breast density can be easily estimated, both of which are of significant interest in determining breast cancer risk. Previous theoretical analysis of SMF had suggested that a complete and substantial set of calibration data (such as mAs and kVp) would be needed to generate realistic breast composition measures and yet there are many interesting trials that have retrospectively collected images with no calibration data. The main contribution of this paper is to revisit our previous theoretical analysis of SMF with respect to errors in the calibration data and to show how and why that theoretical analysis did not match the results from the practical implementations of SMF. In particular, we show how by estimating breast thickness for every image we are, effectively, compensating for any errors in the calibration data. To illustrate our findings, the current implementation of SMF (version 2.2beta) was run over 4028 digitized film-screen mammograms taken from six sites over the years 1988-2002 with and without using the known calibration data. Results show that the SMF implementation running without any calibration data at all generates results which display a strong relationship with when running with a complete set of calibration data, and, most importantly, to an expert's visual assessment of breast composition using established techniques. SMF shows considerable promise in being of major use in large epidemiological studies related to breast cancer which require the automated analysis of large numbers of films from many years previously where little or no calibration data is available.

Algorithms↗

PubMed2006

Initial experiences of using an automated volumetric measure of breast density: the standard mammogram form.

Limitations of area based measures of breast density have led several research groups to develop volumetric measures of breast density, for use in predicting risk and in epidemiological research. In this paper, we describe our initial experiences using an automated algorithm (standard mammogram form, SMF) to estimate the volume of the breast that is dense from digitized film mammograms. We performed analyses on 3816 mammograms of 626 women, who were part of the Glasgow Alumni Cohort and had mammograms taken within the Scottish Breast Screening Programme between 1989 and 2002. Absolute volume of dense breast tissue (SMF volume) and the percentage of the volume of the breast that is dense (SMF%) were calculated. The median (interquartile range) of SMF volume was 66 cm3 (48 to 98), and of SMF% was 23.4% (18.6 to 29.7). SMF%, but not SMF volume, was positively related to a six category classification (SCC) of visually assigned area-based breast density (increase in ln(SMF%) per category increase in SCC: 0.04% (95% CI: 0.03-0.05). The SMF algorithm produced lower SMF volume for craniocaudal (CC) compared with mediolateral oblique (MLO) views, but CC/MLO differences for SMF% were small. The mean right/left difference for ln(SMF volume) was -0.027 cm3 (95% confidence interval (CI) -0.044 to -0.009) and of ln(SMF%) was 0.005% (95% CI -0.008% to 0.019%). We present these initial data as a background for future analytical work using SMF.

Absorptiometry, Photon↗

PubMed2001

A mammographic dilemma: calcification or haemosiderin as a cause of opacities? Validation of a new digital diagnostic tool.

Core biopsies of an area of microcalcification demonstrated large collections of macrophages containing haemosiderin, with evidence of minimal microcalcification on H&E staining. Algorithms were developed that were capable of differentiating with high accuracy those signs due to calcification, using quantitative measurements such as the apparent volume composition of calcium. Using the linear attenuation coefficients of calcification and assuming an ellipsoid model for the 3-dimensional shape of calcification, we computed the relative calcification volume for each region of interest. The difference in the linear attenuation coefficients of iron and calcification allowed the two to be differentiated on a mammogram based on this measure of relative calcification volume.

Breast↗

PubMed2001

Three-dimensional reconstruction of microcalcification clusters from two mammographic views.

Classification of benign/malignant microcalcification clusters is a major diagnostic challenge for radiologists. Clinical studies have revealed that the shape of the cluster, and the spatial distribution of individual microcalcifications within it, are important indicators of its malignancy. However, mammographic images of clustered microcalcifications confound their three-dimensional (3-D) distribution with image projection and breast compression. This paper presents a novel model-based method for reconstructing microcalcification clusters in 3-D from two mammographic views (cranio-caudal and medio-lateral oblique--"shoulder to the opposite hip" or lateral-medio). We develop a 3-D breast representation and a parameterised breast compression model which constraints geometrically the possible 3-D positions of a calcification in a two-dimensional image. Corresponding calcifications in the two views are matched using an estimate of the calcification volume. Both the geometric constraint and the matching criterion are utilized in the final reconstruction step to build the 3-D reconstructed clusters. Validation experiments are described using 30 clusters to verify the individual steps of the model, and results consistent with known ground truth are obtained. Some of the approximations in the model and future work are discussed in the concluding section.

Algorithms↗

PubMed1999

Detecting film-screen artifacts in mammography using a model-based approach.

Microcalcifications can be one of the earliest signs of breast cancer. Unfortunately, their appearance in mammograms can be mimicked by dust and dirt entering the imaging process and this has been shown previously to lead to false positives. We use a model of the imaging process and, in particular, the blurring functions inherent within it to detect the film-screen artifacts caused by dust and dirt and, thus, reduce false-positives. A crucial facet of the work is the choice of the correct image representation upon which to perform the image processing. After extensive testing, our algorithm has identified no microcalcifications as being artifacts and has an artifact detection rate of approaching 96%.

Algorithms↗

PubMed1996

A representation for mammographic image processing.

Mammographic image analysis is typically performed using standard, general-purpose algorithms. We note the dangers of this approach and show that an alternative physics-model-based approach can be developed to calibrate the mammographic imaging process. This enables us to obtain, at each pixel, a quantitative measure of the breast tissue. The measure we use is h(int) and this represents the thickness of 'interesting' (non-fat) tissue between the pixel and the X-ray source. The thicknesses over the image constitute what we term the h(int) representation, and it can most usefully be regarded as a surface that conveys information about the anatomy of the breast. The representation allows image enhancement through removing the effects of degrading factors, and also effective image normalization since all changes in the image due to variations in the imaging conditions have been removed. Furthermore, the h(int) representation gives us a basis upon which to build object models and to reason about breast anatomy. We use this ability to choose features that are robust to breast compression and variations in breast composition. In this paper we describe the h(int) representation, show how it can be computed, and then illustrate how it can be applied to a variety of mammographic image processing tasks. The breast thickness turns out to be a key parameter in the computation of h(int), but it is not normally recorded. We show how the breast thickness can be estimated from an image, and examine the sensitivity of h(int) to this estimate. We then show how we can simulate any projective X-ray examination and can simulate the appearance of anatomical structures within the breast. We follow this with a comparison between the h(int) representation and conventional representations with respect to invariance to imaging conditions and the surrounding tissue. Initial results indicate that image analysis is far more robust when specific consideration is taken of the imaging process and the h(int) representation is used.

Breast Diseases↗