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Philips has designed an optical mammography machine. In this machine the breast is suspended into a cup in which the measurements take place. A special fluid is inserted into the cup to prevent the light from going around the breast instead of going through it but this fluid also weakens the signal. Therefore the cup's shape should be close to the breast's shape. The aim of this project was to design a device to measure the breast's shape. This device was used to perform a pilot study among 18 women, 36 breasts in total. There are several possibilities to create 3D images for example MRI, interfero-metry and triangulation. The method of triangulation is chosen to build a set-up. This set-up contains a video projector to project images into a tank filled with water. Two camera's capture these images. Special software has been written in LabVIEW to create 3D images out of these data. The maximum height of the breast and the FWHM was measured for all breasts. A correlation was found between bra cup size and the maximum height of the breast. On average the breast was 64 mm high, with a standard deviation of 8.5 mm. No correlation was found between the FWHM and a form of bra size or chest circumference. The average FWHM is 7.6 cm with a standard deviation of 2.5 cm.

Rational: An experiment is described whose purpose is to extract those image characteristics that radiologists use in diagnostic decision making. It is one phase in the development of a clinical decisionsupport system to assist radiologists using DCE-MRI (dynamic contrast-enhanced MRI) for breast cancer diagnosis. The results of this experiment will be used to develop a case-based reasoning system, whichrelies on presenting prior similar cases with known diagnosis froma database to aid decision making. Methods: Clinical similarity formass lesions was established by four expert radiologists who systematically sorted lesions visualized by DCE-MRI into similarity clusters using a proprietary software tool. Cognitive analysis was used toidentify the relevant perceptual features characterizing each cluster, such that the list of features and clusters define the clinicalsimilarity. There were no constraints on the number or size of clusters that could be created. The radiologists first individually clustered a total of 214 lesions. A subsequent phase required all radiologists to agree on both a cluster designation and assignment of eachlesion into a cluster. Results: Radiologists created individually10, 10, 12, and 16 clusters. Of this initial cluster assignment, there was unanimous agreement in ~20% of the lesions, and majority agreement of ~60%. The final consensus assignment created 16 clusters; two consisted of all malignant lesions; two consisted of a majority of benign lesions, three were large approximately equal mixes of benign and malignant lesions, while the remaining nine were small clusters representing lesions with clinically relevant special characteristics of low clinical prevalence. Conclusions: The cognitive analysis revealed that the image characteristics differentiating the clusters are highly correlated to BI-RADS lesion descriptors. The radiologists were excellent in clustering certain malignant lesions, very good with some benign lesions; while as expected there was large variability in the majority of lesions.