Computing the scatter component of mammographic images

Abstract

The authors build upon a technical report (Tech. Report OUEL 2009/93, Engng. Sci., Oxford Uni., Oxford, UK, 1993) in which they proposed a model of the mammographic imaging process for which scattered radiation is a key degrading factor. Here, the authors propose a way of estimating the scatter component of the signal at any pixel within a mammographic image, and they use this estimate for model-based image enhancement. The first step is to extend the authors' previous model to divide breast tissue into "interesting" (fibrous/glandular/cancerous) tissue and fat. The scatter model is then based on the idea that the amount of scattered radiation reaching a point is related to the energy imparted to the surrounding neighbourhood. This complex relationship is approximated using published empirical data, and it varies with the size of the breast being imaged. The approximation is further complicated by needing to take account of extra-focal radiation and breast edge effects. The approximation takes the form of a weighting mask which is convolved with the total signal (primary and scatter) to give a value which is input to a "scatter function", approximated using three reference cases, and which returns a scatter estimate. Given a scatter estimate, the more important primary component can be calculated and used to create an image recognizable by a radiologist. The images resulting from this process are clearly enhanced, and model verification tests based on an estimate of the thickness of interesting tissue present proved to be very successful. A good scatter model opens the was for further processing to remove the effects of other degrading factors, such as beam hardening.

<title>Low-dose x-ray imaging GLG: restoration by Kalman filter</title>

“…where the capital letters show the FFT counterparts and (10) Assuming that the components of signal compared with those of noise are superior in the low frequency regions of the Fourier domain, (8) and (9) can be approximated by (12) and hence D1(u,v)…”

A two-stage classifier system for normal mammogram identification

“…We used the techniques described in 12 that models the complete imaging process and compensates for degrading factors, such as scattering. [12][13][14][15] This approach models the complete image creation process starting with the tube voltage and ending with the film or sensor characteristics. The resulting image, known as the h int representation, estimates the height of non-fatty tissue in the breast for each pixel in the image.…”

Mammogram Screening by Automated Followup: A Feasibility Study

“…Detailed description of diagnostic X-ray imaging principles, [21] and basic principles of X-ray mammography [3], [5] are given in the literature. There are various effects of X-ray interaction with human tissue.…”

“…There are various effects of X-ray interaction with human tissue. Three of them occur in the energy range used for diagnostic X-ray imagingphotoelectric effect, coherent, and Compton scattering, [21]. Photoelectric effect is related to Xray attenuation of tissue and absorption of X-rays by tissue.…”