Accurate volumetry of brain tumors in magnetic resonance imaging (MRI) is important for evaluating the interval changes in tumor volumes during and after treatment, and also for planning of radiation therapy. In this study, an automated volumetry method for brain tumors in MRI was developed by use of a new three-dimensional (3-D) image segmentation technique. First, the central location of a tumor was identified by a radiologist, and then a volume of interest (VOI) was determined automatically. To substantially simplify tumor segmentation, we transformed the 3-D image of the tumor into a two-dimensional (2-D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the tumor scanned the 3-D image spirally from the "north pole" to the "south pole". The voxels scanned by the radial line provided a transformed 2-D image. We employed dynamic programming to delineate an "optimal" outline of the tumor in the transformed 2-D image. We then transformed the optimal outline back into 3-D image space to determine the volume of the tumor. The volumetry method was trained and evaluated by use of 16 cases with 35 brain tumors. The agreement between tumor volumes provided by computer and a radiologist was employed as a performance metric. Our method provided relatively accurate results with a mean agreement value of 88&percent;.
A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for
enhancing interval changes on medical images by removing most of normal structures. One of the important problems in
temporal subtraction is that subtraction images commonly include artifacts created by slight differences in the size, shape,
and/or location of anatomical structures. In this paper, we developed a new registration method with voxel-matching
technique for substantially removing the subtraction artifacts on the temporal subtraction image obtained from multiple-detector
computed tomography (MDCT). With this technique, the voxel value in a warped (or non-warped) previous
image is replaced by a voxel value within a kernel, such as a small cube centered at a given location, which would be
closest (identical or nearly equal) to the voxel value in the corresponding location in the current image. Our new method
was examined on 16 clinical cases with MDCT images. Preliminary results indicated that interval changes on the
subtraction images were enhanced considerably, with a substantial reduction of misregistration artifacts. The temporal
subtraction images obtained by use of the voxel-matching technique would be very useful for radiologists in the
detection of interval changes on MDCT images.
The detection of very subtle lesions and/or lesions overlapped with vessels on CT images is a time consuming and
difficult task for radiologists. In this study, we have developed a 3D temporal subtraction method to enhance interval
changes between previous and current multislice CT images based on a nonlinear image warping technique. Our
method provides a subtraction CT image which is obtained by subtraction of a previous CT image from a current CT
image. Reduction of misregistration artifacts is important in the temporal subtraction method. Therefore, our
computerized method includes global and local image matching techniques for accurate registration of current and
previous CT images. For global image matching, we selected the corresponding previous section image for each
current section image by using 2D cross-correlation between a blurred low-resolution current CT image and a blurred
previous CT image. For local image matching, we applied the 3D template matching technique with translation and
rotation of volumes of interests (VOIs) which were selected in the current and the previous CT images. The local shift
vector for each VOI pair was determined when the cross-correlation value became the maximum in the 3D template
matching. The local shift vectors at all voxels were determined by interpolation of shift vectors of VOIs, and then the
previous CT image was nonlinearly warped according to the shift vector for each voxel. Finally, the warped previous
CT image was subtracted from the current CT image. The 3D temporal subtraction method was applied to 19 clinical
cases. The normal background structures such as vessels, ribs, and heart were removed without large misregistration
artifacts. Thus, interval changes due to lung diseases were clearly enhanced as white shadows on subtraction CT
The cardio-thoracic ratio (CTR) is commonly measured manually for the evaluation of cardiomegaly. To determine the CTR automatically, we have developed a computerized scheme based on gray-level histogram analysis and an edge detection technique with feature analysis. The database used in this study consisted of 392 chest radiographs, which included 304 normals and 88 abnormals with cardiomegaly. The pixel size and the quantization level of the image were 0.175 mm and 1024, respectively. We performed a nonlinear density correction to maintain consistency in the density and contrast of the image. Initial heart edge detection was performed by selection of a certain range of pixel values in the histogram of a rectangular area at the center of a low-resolution image. Feature analysis with use of an edge gradient and with the orientation obtained by a Sobel operator was applied for accurate identification of the heart edges, which tend to have large edge gradients in a certain range of orientations. In addition, to determine the CTR, we detected the ribcage edges automatically by using image profile analysis. In 94.9% of all of the cases, the heart edges were detected accurately by use of this scheme. The area under the ROC curve (Az value) in distinguishing between normals and abnormals with cardiomegaly based on the CTR was 0.912. Because the CTR is measured automatically and quickly (in less than 1 sec.), radiologists could save reading time. The computerized scheme will be useful for the assessment of cardiomegaly on chest radiographs.
We have developed a computer-aided diagnostic (CAD) scheme for detection of unruptured intracranial aneurysms in magnetic resonance angiography (MRA) based on findings of short branches in vessel skeletons, and a three-dimensional (3D) selective enhancement filter for dots (aneurysms). Fifty-three cases with 61 unruptured aneurysms and 62 non-aneurysm cases were tested in this study. The isotropic 3D MRA images with 400 x 400 x 128 voxels (a voxel size of 0.5 mm) were processed by use of the dot enhancement filter. The initial candidates were identified not only on the dot-enhanced images by use of a multiple gray-level thresholding technique, but also on the vessel skeletons by finding short branches on parent skeletons, which can indicate a high likelihood of small aneurysms. All candidates were classified into four categories of candidates according to effective diameter and local structure of the vessel skeleton. In each category, a number of false positives were removed by use of two rule-based schemes and by linear discriminant analysis on localized image features related to gray level and morphology. Our CAD scheme achieved a sensitivity of 97% with 5.0 false positives per patient by use of a leave-one-out-by-patient test method. This CAD system may be useful in assisting radiologists in the detection of small intracranial aneurysms as well as medium-size aneurysms in MRA.