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Ultrasound B-scan images often exhibit intensity inhomogeneities caused by non-uniform beam attenuation within the body. These cause major problems for image analysis, both by manual and computer-aided techniques, particularly the computation of quantitative measurements. We present a statistical model that exploits knowledge of tissue properties and intensity inhomogeneities in ultrasound for simultaneous contrast enhancement and image segmentation. The underlying model was originally proposed for correction of the B1 bias field distortion and segmentation of magnetic resonance (MR) images. A physics-based model of intensity inhomogeneities in ultrasound images shows that the bias field correction method is well suited to ultrasound B-scan images. The tissue class labelling and the intensity correction field are estimated using the maximum a posteriori (MAP) principle, in an iterative, multi-resolution manner. The algorithm has been applied to breast and cardiac ultrasound images. The results demonstrate that it can successfully remove intensity inhomogeneities caused by varying attenuation as well as uninteresting intensity changes of background tissues. With the removal of intensity inhomogeneities, significant improvement is achieved in tissue contrast and segmentation result.


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