[Algorithm for automatic determination of the sagittal imaging plane in lumbar MRI].
Explore the source record for details and available documents.
Algorithms↗
Biomedical subjects
Yongbum Lee
Publications and source records attributed to Yongbum Lee.
[Improvement in visibility and detectability of early sign of acute stroke in nonenhanced CT images by using an adaptive partial smoothing filter].
Detection of early infarct signs on nonenhanced CT is mandatory in patients with acute ischemic stroke. Loss of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early infarct sign, which affects decisions on thrombolytic therapy. However, its detection is difficult, since early infarct sign is of subtle hypoattenuation. To improve the detectability of early infarct sign, image processing that could reduce local noise while preserving edges is desirable. To examine this issue, we devised an adaptive partial smoothing filter (APSF). Since the APSF markedly improves visibility of the normal gray-white matter interface, loss of the gray-white matter interface due to hypoattenuation could be more easily detected. The APSF was applied to clinical CT images in hyperacute stroke patients. Our preliminary results showed that the visibility and detectability of early infarct signs was much improved. To validate the usefulness of the proposed method, two commonly used smoothing filters were also employed for comparison. The results demonstrated the superiority of the APSF. Our proposed APSF can improve the visibility of the gray-white matter interface, thereby enhancing the detectability of early infarct signs.
Acute Disease↗
[Improvement in the performance of the adaptive neighborhood contrast enhancement technique based on entropy].
This paper presents an improved adaptive-neighborhood-contrast-enhancement (ANCE) method for the improvement of medical image quality. The ANCE method consists of computing the local contrast around each pixel using a variable neighborhood whose size depends on the statistical properties around the given pixel. The obtained contrast image is then transformed into a new contrast image using a contrast enhancement function. Finally, a contrast-enhanced image is obtained by applying inverse contrast transform to the previous step. This technique provides the advantages of enhancing or preserving image contrast while suppressing noise. However, it does have a drawback. The performance of the ANCE method largely depends on how to determine the parameters used in the processing steps. The present study proposes a novel method for optimal and automatic determination of several parameters using entropy. To quantitatively compare the performance of the proposed method with that of the ANCE method, computer-simulated images are generated. The output-to-input SNR level and the mean squared error are used as comparison criteria. Results demonstrated the superiority of the proposed method. Moreover, we have applied our new algorithm to echocardiograms and mammograms. Our results showed that the proposed method has the potential to become useful for improving the image quality of medical images.
Computer Simulation↗
[Preliminary study on automated detection of cerebral vessels from head CTA images].
We propose an approach for automated detection of cerebral vessels from head CT angiographic images. This approach contains two major features. First, instead of using the well-known image-processing techniques such as thresholding and labeling, a novel Laplacian-like filter is developed and employed in the region of interest in an image to be processed. Second, not only is the axial-view image reconstructed from head CT angiographic images used, but, in addition, the sagittal- and coronal-view images are reconstructed and used. By applying these major features in the process of detection of brain vessels, more accurate results can be achieved. To validate the effectiveness of the proposed method, we applied the method to three clinical cases, all of which were head CT angiograms. Our preliminary results showed that the proposed method has the potential to automatically detect cerebral vessels in head CT angiograms with acceptable accuracy.
Cerebral Angiography↗
[Relationship between line spread function (LSF), or slice sensitivity profile (SSP), and point spread function (PSF) in CT image system.].
In the CT image system, we revealed the relationship between line spread function (LSF), or slice sensitivity profile (SSP), and point spread function (PSF). In the system, the following equation has been reported; I(x,y) = O(x,y) ** PSF(x,y), in which I(x,y) and O(x,y) are CT image and object function, respectively, and ** is 2-dimensional convolution. In the same way, the following 3-dimensional expression applies; I'(x,y,z) = O'(x,y,z) *** PSF'(x,y,z), in which z-axis is the direction perpendicular to the x/y-scan plane. We defined that the CT image system was separable, when the above two equations could be transformed into following equations; I(x,y) = [O(x,y) * LSF(x)(x) ] * LSF(y)(y) and I' (x,y,z) = [ O'(x,y,z) * SSP(z) ] ** PSF(x,y), respectively, in which LSF(x)(x) and LSF(y)(y) are LSFs in x- and y-direction, respectively. Previous reports for the LSF and SSP are considered to assume the separable-system. Under the condition of separable-system, we derived following equations; PSF(x,y)=LSF(x)(x) LSF(y)(y) and PSF' (x,y,z) = PSF(x,y) SSP(z). They were validated by the computer-simulations. When the study based on 1-dimensional functions of LSF and SSP are expanded to that based on 2- or 3-dimensional functions of PSF, derived equations must be required.
Computer Simulation↗
[An automated detection of lacunar infarct regions in brain MR images: preliminary study].
The purpose of this study is to develop a technique to detect lacunar infarct regions automatically in brain MR images. Our detection method is based on the definition of lacunar infarcts. After inputted images were binarized, we used feature values such as area, circularities and the center of gravity of candidate regions to extract isolated lacunar infarct regions. We also developed and used a new filter to enhance the signals of lacunar infarcts adjacent to some high intensity regions. 10 cases involving 81 sectional images were applied to our experiment. As a result, the sensitivity was 100% with approximately 1.77 false-positives per image. Our results are promising on the first stage, although it remains to improve on problems that to eliminate false-positives and automatically establish threshold value.
Cerebral Infarction↗