Computed tomography (CT)-based virtual colonoscopy or CT colonography (CTC) currently utilizes oral contrast
solutions to tag the colonic fluid and possibly residual stool for differentiation from the colon wall and polyps. The
enhanced image density of the tagged colonic materials causes a significant partial volume (PV) effect into the colon
wall as well as the lumen space (filled with air or CO2). The PV effect on the colon wall can "bury" polyps of size as
large as 5mm by increasing their image densities to a noticeable level, resulting in false negatives. It can also create
false positives when PV effect goes into the lumen space. We have been modeling the PV effect for mixture-based
image segmentation and developing text-based computer-aided detection of polyp (CADpolyp) by utilizing the PV
mixture-based image segmentation. This work presents some preliminary results of developing and applying texture-based
CADpolyp technique to low-dose CTC studies. A total of 114 studies of asymptomatic patients older than 50,
who underwent CTC and then optical colonoscopy (OC) on the same day, were selected from a database, which was
accumulated in the past decade and contains various bowel preparations and CT scanning protocols. The participating
radiologists found ten polyps of greater than 5 mm from a total of 16 OC proved polyps, i.e., a detection sensitivity of
63%. They scored 23 false positives from the database, i.e., a 20% false positive rate. Approximately 70% of the
datasets were marked as imperfect bowel cleansing and/or presence of image artifacts. The impact of imperfect bowel
cleansing and image artifacts on VC performance is significant. The texture-based CADpolyp detected all the polyps
with an average of 2.68 false positives per patient. This indicates that texture-based CADpolyp can improve the CTC
performance in the cases of imperfect cleansed bowels and presence of image artifacts.
Computed tomography-based virtual colonoscopy or CT colonography (CTC) currently utilizes oral contrast solution to
differentiate the colonic fluid and possibly residual stool from the colon wall. The enhanced image density of the tagged
colonic materials causes a significant partial volume (PV) effect into the colon wall as well as the lumen space (air or
CO2). The PV effect into the colon wall can "bury" polyps of small size by increasing their image densities to a
noticeable level, resulting in false negatives. It can also create false positives when PV effect goes into the lumen space.
Modeling the PV effect for mixture-based image segmentation has been a research topic for many years. This paper
presents the practical implementation of our newly developed statistical image segmentation framework, which utilizes
the EM (expectation-maximization) algorithm to estimate (1) tissue fractions in each image voxel and (2) statistical
model parameters of the image under the principle of maximum a posteriori probability (MAP). This partial-volume
expectation-maximization (PV-EM) mixture-based MAP image segmentation pipeline was tested on 52 CTC datasets
downloaded from the website of the VC Screening Resource Center, with each dataset consisting of two scans of supine
and prone positions, resulting in 104 CT volume images. The cleansed lumens by the automated PV-EM image
segmentation algorithm were visualized with comparison to our previous work, with the gain achieved mainly in the
following three aspects: (1) the tissue fraction information of those voxels with PV effect have been well preserved, (2)
the problem of incomplete cleansing of tagged materials in our previous work has been mitigated, and (3) the
interference caused by small bowel was significantly released.
In this paper, we propose a new technique to utilize both the morphological and the texture information of the colon
wall for detection of colonic polyps. Firstly this method can quickly identify suspicious patches of the colon wall by
employing special local and global geometrical information, different from other methods of utilizing local geometry
only. By our edge-detection technology, the growing region of suspected polyps is identified and its internal textures
are quantitatively analyzed based on an assumed ellipsoid polyp model. Both the extracted texture and morphological
information are then applied to eliminate the false positives from the identified suspicious patches. With all the
extracted geometrical, morphological and texture features, this presented computer-aided detection method have
demonstrated significant improvement in detection of the colonic polyps for virtual colonoscopy.
In this paper, we proposed a new efficient implementation for simulation of surgery planning for congenital aural
atresia. We first applied a 2-level image segmentation schema to classify the inner ear structures. Based on it, several
3D texture volumes were generated and sent to graphical pipeline on a PC platform. By exploiting the texturingmapping
capability on the PC graphics/video board, a 3D image was created with high quality showing the accurate
spatial relationships of the complex surgical anatomy of congenitally atretic ears. Furthermore, we exploited the
graphics hardware-supported per-fragment function to perform the geometric clipping on 3D volume data to
interactively simulate the procedure of surgical operation. The result was very encouraging.
We propose a new partial volume (PV) segmentation scheme to extract bladder wall for computer aided detection (CAD) of bladder lesions using multispectral MR images. Compared with CT images, MR images provide not only a better tissue contrast between bladder wall and bladder lumen, but also the multispectral information. As multispectral images are spatially registered over three-dimensional space, information extracted from them is more valuable than that extracted from each image individually. Furthermore, the intrinsic T1 and T2 contrast of the urine against the bladder wall eliminates the invasive air insufflation procedure. Because the earliest stages of bladder lesion growth tend to develop gradually and migrate slowly from the mucosa into the bladder wall, our proposed PV algorithm quantifies images as percentages of tissues inside each voxel. It preserves both morphology and texture information and provides tissue growth tendency in addition to the anatomical structure. Our CAD system utilizes a multi-scan protocol on dual (full and empty of urine) states of the bladder to extract both geometrical and texture information. Moreover, multi-scan of transverse and coronal MR images eliminates motion artifacts. Experimental results indicate that the presented scheme is feasible towards mass screening and lesion detection for virtual cystoscopy (VC).
In this paper, we propose a new computer aided detection (CAD) technique to utilize both global and local shape information of the colon wall for detection of colonic polyps. Firstly, the whole colon wall is extracted by our mixture-based image segmentation method. This method uses partial volume percentages to represent the distribution of different materials in each voxel, so it provides the most accurate information on the colon wall, especially the mucosa layer. Local geometrical measure of the colon mucosa layer is defined by the curvature and gradient information extracted from the segmented colon-wall mixture data. Global shape information is provided by applying an improved linear integral convolution operation to the mixture data. The CAD technique was tested on twenty patient datasets. The local geometrical measure extracted from the mixture segmentation represents more accurately the polyp variation than that extracted from conventional label classification, leading to improved detection. The added global shape information further improves the polyp detection.
In this paper, a new feature based rendering algorithm for partial volume is presented. This algorithm utilizes both surface and volume information for the rendering of the partial volume segmentation data with associated features. First principal directions are extracted from the partial volume segmentation dataset to construct a feature vector dataset. Using the improved LIC (line integral convolution) algorithm, our rendering algorithm can integrate the feature dataset into the traditional volume rendering process. Combining the features and the traditional volume rendering, the partial volume could be visualized clearly. This can result in an improved diagnosis in clinic, because the partial volume quantities carry very rich diagnostic information.
Light field algorithm is one of the most famous image-based rendering techniques. In this paper, an improved light field algorithm - sphere light field algorithm - is proposed. This new algorithm replaces the parallel planes used in traditional light field algorithms by a triangularly parameterized sphere surface. Comparing to the traditional light field algorithms, this new algorithm achieves a more uniform distribution of light slabs in the three dimensional space. This improves the quality of the rendered images. Using the triangular parameterization and subdivision of the whole light field, less calculation and less memory are needed, resulting in improved real-time rendering.
KEYWORDS: Image segmentation, 3D image processing, 3D modeling, Ultrasonography, 3D displays, Image processing algorithms and systems, Computer simulations, Visualization, Surgery, Data modeling
Stenosis of the carotid is the most common cause of the stroke. The accurate measurement of the volume of the carotid and visualization of its shape are helpful in improving diagnosis and minimizing the variability of assessment of the carotid disease. Due to the complex anatomic structure of the carotid, it is mandatory to define the initial contours in every slice, which is very difficult and usually requires tedious manual operations. The purpose of this paper is to propose an automatic segmentation method, which automatically provides the contour of the carotid from the 3-D ultrasound image and requires minimum user interaction. In this paper, we developed the Geometrically Deformable Model (GDM) with automatic merge function. In our algorithm, only two initial contours in the topmost slice and four parameters are needed in advance. Simulated 3-D ultrasound image was used to test our algorithm. 3-D display of the carotid obtained by our algorithm showed almost identical shape with true 3-D carotid image. In addition, experimental results also demonstrated that error of the volume measurement of the carotid based on the three different initial contours is less that 1% and its speed was a very fast.
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