Volume preserving elastic transformation for local breast-tissue quantification Kostas Marias a, b * , Christian Behrenbruch a , Ralph Highnam c , Michael Brady a , Santilal Parbhoo b , Alexander Seifalian b a Robotics Research Group, Engineering Science, Oxford University, Parks Road, Oxford, OX1 3PJ, b Department of Surgery, Royal Free and University College Medical School, UCL, London NW3 2QG, c MIRADA Solutions Limited, Oxford Centre for Innovation, Mill Street, OX2 0JX. Abstract. In this paper, we explore the idea of quantifying local breast-tissue density changes. Breast tissue density has been correlated to breast cancer incidence in numerous studies which have shown a statistical relationship between glandular density and the occurrence of cancer. In particular, postmenopausal women who take HRT run an increased risk of developing cancer due to the “regeneration” of fibroglandular tissue that is often induced by the exogenous hormones. In this paper, we present a method that combines mammogram normalisation and volume-preserving registration, and which can be the starting point for temporal-local breast tissue quantification. 1 Introduction Hormone Replacement Therapy (HRT) has many beneficial effects for post-menopausal women (e.g. reduction of menopause-related symptoms, and lesser risk of developing osteoporosis). Unfortunately, long-term use has been correlated with an increased risk of breast cancer [1]. Glandular tissue regeneration is often a sign that the woman’s breasts are responding to the exogenous hormones and it is crucial to closely monitor the patient in such cases, with the goal of an early diagnosis of a possible HRT-induced cancer. Our objective is to quantify local tissue density changes for women taking HRT using only mammogram data. To achieve this goal, two sub-goals are necessary: • Computation of the h int mammogram representation [2]. Due to different imaging conditions (e.g. time of exposure) and image degrading factors (e.g. scattering), result in poor intensity correspondence in temporal mammograms. The h int representation estimates the thickness of non-fatty tissue “above” each pixel, by using a physical model of the image formation [2]. This way, we have a quantitative representation of the breast which effectively normalises temporal mammogram pairs. Based on this representation (of integrated non-fatty breast tissue), we have calculated measures of tissue density change by exploiting the fact that a global or local change in a specific h int mammogram area reflects the change in the fibroglandular composition of the breast or of that area [3]. Since the sum of h int values (total non-fatty tissue) is expected to be invariant between acquisitions, we are interested in cases where this does not hold (possible pathology, response to HRT). • Image registration. This is necessary in order to align the two mammograms, for quantification of change in local tissue density. We have developed a method for mammogram registration, based on breast boundary landmarks (detected using curvature) and internal landmarks (using a multi-scale segmentation framework) [4]. However, the elastic registration process involves pixel rearrangement and scaling which can significantly alter the total “volume” of the mammogram image. The later can be defined as: V Image ∑ = = N i i i y x I 0 ) , ( Where N is the number of pixels across the image. This problem is illustrated in Figure 1, where the “volume” of a synthetic image is reduced to 70% of the original, after applying a transformation. In many cases, due mainly to temporal differences in the breast size/compression, the mammogram size and geometry can change significantly between acquisitions. Consequently, aligning temporal data can reduce (or increase) the image volume. This is not important for “un- normalised” mammograms since the intensities of corresponding regions are not necessarily related. However, * Author for correspondence, {kostas, cpb}@robots.ox.ac.uk