<title>Contrast enhancement and segmentation of ultrasound images: a statistical method</title>

Abstract

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.

Evaluation of Cardiac Ultrasound Data by Bayesian Probability Maps

“…This may be attributed to the very noisy nature of these sequences. This comparison is limited and given to show the differences between the proposed method and pure Graph Cut algorithms, as there are some fundamental similarities such as pixel dependencies; similar to the methods described in [6,7,[9][10][11], the Graph Cut method uses MRF for this. However, more complex methods share many similarities with our method, e.g.…”

“…A similar approach is employed in [6][7][8][9][10], where Markov random field (MRF) regularization is used. Like our model, in [7,[9][10][11] a Bayesian framework is used, although the construction of the posterior density function is different. Our approach uses priors on location and shape; of the forementioned, only one work [9] uses a shape prior.…”

Detection and classification of masses in breast ultrasound images

“…It has been shown that sonography is superior to mammography in two aspects: (1) its ability to detect focal abnormalities in the dense breasts of adolescent women [22]; and (2) the fact that ultrasound images are acquired with a relatively lower health risk to the patient, and at a lower cost [25]. Sonography is an important adjunct to mammography in breast cancer detection and has been especially useful in distinguishing cysts from solid tumors.…”

CAD Algorithms for Solid Breast Masses Discrimination: Evaluation of the Accuracy and Interobserver Variability

“…Sometimes, the edges of the breast tissue and masses were blurry, the contrast was low and the masses usually could not be well analyzed. In addition, it has a limitation in detecting microcalcifications (Xiao et al 2000). With the development of computer applications, many imaging tools based on computer-aided diagnosis (CAD) technologies were developed to enhance the physician's diagnostic accuracy.…”