The dual or multi-energy x-ray technique facilitates to generate tissue-selective images, by exploiting tissue-specific
energy dependence of x-ray attenuation. An abnormal breast is considered to be a mixture of adipose, glandular, and
abnormal tissues, but three tissues cannot be selectively decomposed because the total attenuation of a tissue is
represented by only two attenuation basis functions at diagnostic energy range. This paper presents a novel method to
selectively represent abnormal breast tissue, using polyenergetic multi-energy x-ray. We show that an abnormal tissue
can be revealed from the total thickness map, which is virtually constructed by assuming a healthy and compressed
breast. Specifically, regression analysis is first performed using the multi-energy images of the prepared calibration
phantom that consists of two basis materials. Total thickness map is then constructed by linearly combining thickness
maps of basis materials, where the optimal weights for combination are determined so that the uniformity of total breast
thickness is maximized. It is noted that the proposed method does not need accurate attenuation coefficients of breast
tissues. Simulation results show that the proposed method dramatically improves the detectability of mass that is
obscured by normal structures.
Spectral X-ray imaging is a promising technique to drastically improve the diagnostic quality of radiography and
computed tomography (CT), since it enables material decomposition and/or identification based on the energy
dependency of material-specific X-ray attenuation. Unlike the
charge-integration based X-ray detectors, photon counting
X-ray detectors (PCXDs) can discriminate the energies of incident
X-ray photons and thereby multi-energy images can
be obtained in single exposure. However, the measured data are not accurate since the spectra of incident X-rays are
distorted according to the energy response function (ERF) of a PCXD. Thus ERF should be properly estimated in
advance for accurate spectral imaging. This paper presents a simple method for ERF estimation based on a
polychromatic X-ray source that is widely used for medical imaging. The method consists of three steps: source spectra
measurement, detector spectra reconstruction, and ERF inverse estimation. Real spectra of an X-ray tube are first
measured at all kVs by using an X-ray spectrometer. The corresponding detector spectra are obtained by threshold scans.
The ERF is then estimated by solving the inverse problem. Simulations are conducted to demonstrate the concept of the
We propose a two-stage real-time tracking algorithm for an active camera system having pan-tilt-zoom functions. The algorithm is based on the assumption that a human head has an elliptical shape and that its model color histogram has been acquired in advance. The algorithm consists of two stages, a color-based convergence stage for fast and reliable tracking and a refinement stage for accurate tracking based on multimodal information. In the first color convergence stage, we roughly estimate the target position by using the mean-shift method based on the histogram similarity between the model and a candidate ellipse. To better predict the initial position for the mean shift, the global motion is compensated; to enhance reliability of the mean shift, the model histogram is appropriately updated by referring to the target histogram in the previous frame. In the subsequent refinement stage, we refine the position and size of the ellipse obtained at the first stage by using multimodal information such as color, shape, and quasi-spatial information. In particular, to quantify the quasi-spatial information, we use a spatial color histogram obtained by properly dividing the ellipse into two regions. Extensive experiments verify that the proposed algorithm robustly tracks the head, even when the subject moves quickly, the head size changes drastically, or the background has many clusters and/or distracting colors. Also, the proposed algorithm can perform real-time tracking with a processing speed of about 10 fps on a standard PC.
Diameters (or areas) of vessel cross-sections provide useful information for diagnosis and surgery planning. However, the ordinary centerline-perpendicular cross-sections are often inappropriate to use because the centerline may include unwanted local curvatures in irregular or asymmetric regions and high curvatures in sharply bended regions. In this paper, we try to improve the accuracy of vessel cross-section measurement by properly adjusting the centerline. To alleviate local curvatures in the centerline while preserving the global shape faithfully, we register a deformable cylindrical model onto the vessel lumen, and subsequently adopt the axis of the registered model as the adjusted centerline for determining cross-sections. In addition, by introducing the electric field model, we prevent undesirable intersection of cross-sections that is often found in sharply bended regions. Experiments are performed using various synthesized images that simulate abnormal vessels with stenoses or aneurysms. The results show that the registration process successfully eliminates unwanted local curvatures while preserving the global shape of the vessel, and obtained cross-sections do not intersect each other even in the region of high curvature.
For accurate determination of thickness-profile in vessel quantification, it is important to find appropriate vessel cross-sections. To obtain vessel cross-sections, a centerline-based approach has been widely used, but it has several inherent problems causing improper cross-sections. First, this approach cannot define cross-sections in a unique way. Second, cross-sections are sensitive to the degree of smoothness of a detected vessel centerline. Third, a small variation in a centerline causes a considerable change in the resultant cross-sections and this phenomenon brings about improper cross-sections in the abnormal vessel of asymmetric structure. Finally, wrong cross-sections may be detected due to the intersection with the other cross-sections in a region of high curvature. In this paper, instead of a centerline, we propose and adopt a complementary geodesic distance field. Then, we detect a sequence of equidistant lines by using the proposed distance field. Finally, we determine cross-sections by refining the obtained equidistant lines. Due to the prospective properties of the proposed distance field, we can alleviate all of the conventional problems and obtain the cross-sections more proper for vessel quantification. Through the intensive simulation using various 2-D synthesized images, we prove that the proposed method provides non-intersecting cross-sections which are insensitive to local variation of geometrical shapes in abnormal vessels.
In this paper, we propose a robust real-time head tracking algorithm using a pan-tilt-zoom camera. In the algorithm, the shape of the head is assumed as an ellipse and a model color histogram is acquired in advance. Then, in the first frame, a user defines the position and scale of a head. In the following frame, we consider the ellipse selected in the previous frame as the initial position, and apply the mean-shift procedure to move the position to the target center where the color histogram similarity to the reference one is maximized. Here, the reference color histogram is adaptively updated from the model color histogram by using the one in the previous frame. Then, by using gradient information, the position and scale of the ellipse are further refined. Large background motion often makes the initial position not converge to the target position. To alleviate this problem, we estimate a reliable initial position by compensating the background motion. Here, to reduce the computational burden of motion estimation, we use the vertical and horizontal 1-D projection dataset. Extensive experiments show that a head is well tracked even when a person moves fast and the scale of the head changes drastically.
To navigate a colon lumen, a proper camera path should be generated prior to the navigation. Conventional path-planning algorithms try to find an accurate and robust centerline by assuming that a centerline of colon lumen is the best choice for camera path. For efficient and reliable navigation, however, the centerline may not minimize unobservable area from the camera path. In this paper, we first define a new coverage measure reflecting the temporal visibility. And based on this measure, a fast and efficient path-planning algorithm is proposed to increase the visibility coverage. The proposed algorithm first simplifies the object surface using the centerline determined. Then, camera view positions and directions are estimated to maximize the observable surface. Simulation results prove that the proposed algorithm provides a better coverage rate than the conventional one without a significant increase of additional computation.
Virtual colonoscopy is a computerized procedure to examine colonic polyps from a CT data set. To automatically fly through a long and complex-shaped colon with a virtual camera, we propose an efficient method to simultaneously generate view-positions and view-directions. After obtaining a 3-D binary colon model, we find an initial path that represents rough camera directions and positions along it. Then, by using this initial path, we generate control planes to find a set of discrete view-positions, and view planes to obtain the corresponding view-directions, respectively. Finally, for continuous and smooth navigation, the obtained view-positions and directions are interpolated using the B-spline method. Here, by imposing a constraint to control planes, penetration and collision can be avoided in the interpolated result. Effectiveness of the proposed algorithm is examined via computer simulations using the several phantoms to simulate the characteristics of human colon, namely, high-curvatures and complex structure. Simulation results show that the algorithm provides the view-positions and view-directions suitable for covering more 3-D surface area in the navigation. Also, prospective results are obtained for human colon data with a high processing speed of less than 1 minute with a 2 GHz standard PC.