This paper proposes a hybrid active contour model for inhomogeneous image segmentation. The data term of the energy function in the active contour consists of a global region fitting term in a difference image and a local region fitting term in the original image. The difference image is obtained by subtracting the background from the original image. The background image is dynamically estimated from a linear filtered result of the original image on the basis of the varying curve locations during the active contour evolution process. As in existing local models, fitting the image to local region information makes the proposed model robust against an inhomogeneous background and maintains the accuracy of the segmentation result. Furthermore, fitting the difference image to the global region information makes the proposed model robust against the initial contour location, unlike existing local models. Experimental results show that the proposed model can obtain improved segmentation results compared with related methods in terms of both segmentation accuracy and initial contour sensitivity.
Calculation of ultrasonic field based on medical transducers is often done by applying acoustics and using the Tupholestetpanishen method of calculation. The calculation is based on spatial impulse response; the spatial impulse response has only been determined analytical for a few geometries and using apodization over the transducer surface generally make its impossible to find the response analytically. A popular approach to find the general field is thus to split the aperture into small rectangles, and then sum the weighted response from each of these. The problem with triangular is their poor fit apertures which do not have straight edges, such as circular and oval shapes. In order to solve the problem, a novel algorithm based on triangular be proposed in the paper, the simulation of ultrasonic field based on the algorithm can be improved obviously.
This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided
by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing
the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the
original image are recorded in the model. The hierarchical relation and mean location relation between different organs
are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of
different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation
method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The
hierarchical structure and location relation integrated in the model provide the initial parameters for registration and
make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures
that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented
and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.
An anisotropic optical flow estimation method based on self-adaptive cellular neural networks (CNN) is proposed. First, a novel optical flow energy function which contains a robust data term and an anisotropic smoothing term is projected. Next, the CNN model which has the self-adaptive feedback operator and threshold is presented according to the Euler–Lagrange partial differential equations of the proposed optical flow energy function. Finally, the elaborate evaluation experiments indicate the significant effects of the various proposed strategies for optical flow estimation, and the comparison results with the other methods show that the proposed algorithm has better performance in computing accuracy and efficiency.
A direct method of motion estimation and structure reconstruction for various motion forms has been proposed based on optical flow. First, a motion estimation energy function is established by redesigning the data term and the smoothing term under the assumption of three-dimensional motion constancy. Next, the Euler-Lagrange iterative formula for motion estimation is obtained with the variational method. Finally, the relative depth and a dense structure are recovered from the estimated motion velocities. Experimental results show that the proposed method is more precise and robust for estimating and reconstructing various motion forms.
In this paper, a novel region-based active surface model in a level set framework is proposed for subthalamic nucleus
segmentation on MRI. The method is an extension of region based active contour in which the joint prior information of
the object shape and the imaging intensity are utilized to drive the surface evolution in a level set formulation for
segmentation. The mean surface area calculated from labeled samples is used as prior constraints of the object to
segment. This feature and the intensity difference between object and background define a region-based force that drives
a set of 3D surfaces towards the optimal segmentation. Specially, the pre-segmentation of visible structure within the
region of interest constitutes an important step of subthalamic nucleus segmentation and the result forms confinement for
the evolution domain of the surface, which enhances the validity of data term in the model.
Coronary angiogram is an important examination tool in clinical medicine for the precise diagnosis of cardiac disease. It
is obtained by injecting of the patient with a contrast medium through a catheter. This paper presents a method to
increase vessel contrast and to attenuate background. The enhancement is achieved by subtracting the estimated
background from a live (contrast-containing) angiogram. The
multi-scale morphology opening, with structuring elements
of different dimension for each pixel, is employed to get the estimation of background. The dimension of structuring
element for each pixel is calculated by the response difference between opening filtering results of original image with
different structuring elements. The proposed algorithm is tested on real x-ray angiogram.