A novel clinical prototype, CURE (Computed Ultrasound Risk Evaluation), is used to collect breast tissue image data of patients with either benign or malignant masses. Three types of images, reflection, sound speed and attenuation, are generated from the raw data using tomographic reconstruction algorithms. Each type of image, usually presented as a gray scale image, maps different characteristics of the breast tissue. This study is focused on fusing all three types of images to create true color (RGB) images by assigning a different primary color to each type of image. The resulting fused images display multiple tissue characteristics that can be viewed simultaneously. Preliminary results indicate that it may be possible to characterize breast masses on the basis of viewing the superimposed information. Such a methodology has the potential to dramatically reduce the time required to view all the acquired data and to make a clinical assessment. Since the color scale can be quantified, it may also be possible to segment such images in order to isolate the regions of interest and to ultimately allow automated methods for mass detection and characterization.
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