• Korean Journal of Radiology
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• v.1 no.2
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• pp.110-113
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• 2000

Objective: The purpose of this study was to determine, when measuring prostate volume by TRUS, whether height is more accurately determined by transaxial or midsagittal scanning. Materials and Methods: Sixteen patients who between March 1995 and March 1998 underwent both preoperative TRUS and radical prostatectomy for prostate cancer were included in this study. Using prolate ellipse volume calculation (height × length × width × 𝜋/6), TRUS prostate volume was determined, and was compared with the measured volume of the specimen. Results: Prostate volume measured by TRUS, regardless of whether height was determined transaxially or midsagittally, correlated closely with real specimen volume. When height was measured in one of these planes, a paired t test revealed no significant difference between TRUS prostate volume and real specimen volume (p = .411 and p = .740, respectively), nor were there significant differences between the findings of transaxial and midsagittal scanning (p = .570). A paired sample test, however, indicated that TRUS prostate volumes determined transaxially showed a higher correlation coefficient (0.833) and a lower standard deviation (9.04) than those determined midsagittally (0.714 and 11.48, respectively). Conclusion: Prostate volume measured by TRUS closely correlates with real prostate volume. Furthermore, we suggest that when measuring prostate volume in this way, height is more accurately determined by transaxial than by midsagittal scanning.

• Korean Journal of Radiology
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• v.2 no.3
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• pp.159-163
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• 2001

Objective: The purpose of this study is to correlate the findings of peripheral hypoechoic rim, seen at transrectal ultrasonography (TRUS) in chronic prostatitis patients, with the histopthologic findings. Materials and Methods: Seven patients with pathologically proven chronic prostatitis were involved in this study. The conspicuity of the peripheral hypoechoic prostatic rim, seen at TRUS, was prominent and subtle, and to determine its histopathologic nature, the microscopic findings were reviewed. Results: In five of seven cases (71%), TRUS demonstrated a prominent peripheral hypoechoic rim. Microscopic examination revealed that inflammatory cell infiltration of prostatic glandular tissue was severe in three cases (42.9%), moderate in two (28.6%), and minimal in two (28.6%). In all seven cases, the common histopathologic findings of peripheral hypoechoic rim on TRUS were loose stromal tissues, few prostatic glands, and sparse infiltration by inflammatory cells. Conclusion: The peripheral hypoechoic rim accompanying prostatic inflammation and revealed by TRUS reflects a sparsity of prostate glandular tissue and is thought to be an area in which inflammatory cell infiltration is minimal.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.101-109
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• 2012

In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation inTRUS images using support vectors and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. Gabor filter bank for extracting the texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. The boundary of prostate is extracted by the snake-like contour algorithm. The results showed that this new algorithm extracted the prostate boundary with less than 9.3% relative to boundary provided manually by experts.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.7
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• pp.3087-3091
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• 2014

Purpose: To determine the utility of digital rectal examination (DRE), serum total prostate specific antigen (tPSA) estimation, and transrectal ultrasound (TRUS) for the detection of prostate cancer (PCa) in men with lower urinary tract symptoms (LUTS). Materials and Methods: All patients with abnormal DRE, TRUS, or serum tPSA >4ng/ml, in any combination, underwent TRUS-guided needle biopsy. Eight cores of prostatic tissue were obtained from different areas of the peripheral prostate and examined histopathologically for the nature of the pathology. Results: PCa was detected in 151 (50.3%) patients, remaining 149 (49.7%) showed benign changes with or without active prostatitis. PCa was detected in 13 (56.5%), 9 (19.1%), 26 (28.3%), and 103 (74.6%) of patients with tPSA <4 ng/ml, 4-10 ng/ml, 10-20 ng/ml and >20 ng/ml respectively. Only 13 patients with PCa had abnormal DRE and TRUS with serum PSA <4 ng/ml. The detection rate was highest in patients with tPSA >20 ng/ml. The association between tPSA level and cancer detection was statistically significant (p<0.01). Among 209 patients with abnormal DRE and raised serum PSA, PCa was detected in 128 (61.2%). Conclusions: The incidence of PCa increases with increasing serum level of tPSA. The overall screening and detection rate can be further improved by using DRE, TRUS and TRUS-guided prostate needle biopsies.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.675-682
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• 2014

Prostate is only an organ of men. To diagnose the disease of the prostate, generally transrectal ultrasound(TRUS) images are used. Detecting its boundary is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Support Vector Machine(SVM) is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing. Gabor filter bank for extraction of rotation-invariant texture features has been implemented. SVM for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted. A number of experiments are conducted to validate this method and results shows that the proposed algorithm extracted the prostate boundary with less than 10% relative to boundary provided manually by doctors.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.187-194
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• 2012

Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease, transrectal ultrasound(TRUS) images are being used because the cost is low. But, accurate detection of prostate boundaries is a challenging and difficult task due to weak prostate boundaries, speckle noises and the short range of gray levels. This paper proposes a method for automatic prostate segmentation in TRUS images using its average shape model and invariant features. This approach consists of 4 steps. First, it detects the probe position and the two straight lines connected to the probe using edge distribution. Next, it acquires 3 prostate patches which are in the middle of average model. The patches will be used to compare the features of prostate and nonprostate. Next, it compares and classifies which blocks are similar to 3 representative patches. Last, the boundaries from prior classification and the rough boundaries from first step are used to determine the segmentation. A number of experiments are conducted to validate this method and results showed that this new approach extracted the prostate boundary with less than 7.78% relative to boundary provided manually by experts.

• Journal of Yeungnam Medical Science
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• v.21 no.2
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• pp.191-197
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• 2004

Background: This study was performed to reconsider the efficacy of transrectal ultrasonography (TRUS) in diagnosing prostate cancer by analyzing the results of a digital rectal examination (DRE), serum prostate-specific antigen (PSA) and a transrectal ultrasonography in patients with prostate specific antigen levels of 10 ng/ml or less. Materials and Methods: One-hundred and eighty one men with PSA levels of 10 ng/ml or less, who had a TRUS-guided tissue biopsy performed, were included in this study. The detection rate of prostate cancer was compared according to the TRUS result and the presence or absence of nodularity and the consistency of the prostate on DRE. Results: In a total 181 patients, there were 73 patients with PSA levels of 4 ng/ml or less and 4 of them had prostate cancer. Thre were 108 patients with PSA levels of 4-10 ng/ml and 18 of them were prostate cancer. TRUS was performed in 152 patients and 16 out of 58 patients diagnosed with prostate cancer, 3 out of 39 diagnosed with suspicious prostate cancer, and 2 out of 55 patients diagnosed as having no prostate cancer were found to have prostate cancer. In 40 patients, a nodule was palpated on DRE and 8 of them were found to have prostate cancer. Five out of 19 patients with a stony hard consistency, 3 of 12 with a firm to hard consisency, 12 of 129 with a firm consistency, 0 of 13 with a soft to firm consistency, and 2 of 8 with a soft consistency were prostate cancer. In the prostate cancer patients, there were 4 patients with PSA levels of 4 ng/ml or less and all these patients were diagnosed with prostate cancer or suspicious prostate cancer on TRUS but the nodule was not palpated in all patients. Two were soft and 2 were firm consistency on DRE. Conclusion: In patients with serum PSA levels of 10 ng/ml or less, TRUS is a more useful supporting method than DRE and a more active application of TRUS may lead to an early diagnosis and pertinent treatment of prostate cancer.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.272-288
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• 2014

This paper presents a two-step, semi-automated method for reconstructing a three-dimensional (3D) shape of the prostate from a 3D transrectal ultrasound (TRUS) image. While the method has been developed for prostate ultrasound imaging, it can potentially be applicable to any other organ of the body and other imaging modalities. The proposed method takes as input a 3D TRUS image and generates a watertight 3D surface model of the prostate. In the first step, the system lets the user visualize and navigate through the input volumetric image by displaying cross sectional views oriented in arbitrary directions. The user then draws partial/full contours on selected cross sectional views. In the second step, the method automatically generates a watertight 3D surface of the prostate by fitting a deformable spherical template to the set of user-specified contours. Since the method allows the user to select the best cross-sectional directions and draw only clearly recognizable partial or full contours, the user can avoid time-consuming and inaccurate guesswork on where prostate contours are located. By avoiding the usage of noisy, incomprehensible portions of the TRUS image, the proposed method yields more accurate prostate shapes than conventional methods that demand complete cross-sectional contours selected manually, or automatically using an image processing tool. Our experiments confirmed that a 3D watertight surface of the prostate can be generated within five minutes even from a volumetric image with a high level of speckles and shadow noises.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.214-221
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• 2001

The Great Hall of Incheon International Airport Transportation Center has irregular curved roof structure. The structure consists of 13 main steel trusses. (the longest span of 162m, 480ton) The total weight of the truss is 6,300ton and the whole truss is made of 9,600 pieces which has the joint connected to maximum 13 different parts at one point and Roof Truss is supported by 12 Fabric Foundation. Considering the economical efficiency and the schedule, the Sliding Construction Method was used other than conventional erection method. The roof truss structure was divided into two blocks, 3,550ton and 2,700ton, each block was pre-erected on the Giant Sleigh off the site and was pulled 181m by using Tandem Pulling Jack and Strand to be set in place.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.5
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• pp.187-194
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• 2014

Prostate cancer is a malignant tumor occurring in the prostate. Recently, the repetition rate is increasing. Image inspection method which we can check the prostate structure the most correctly is MRI(Magnetic Resonance Imaging), but it is hard to apply it to all the patients because of the cost. So, they use mostly TRUS(Transrectal Ultrasound) images acquired from prostate ultrasound inspection and which are cheap and easy to inspect the prostate in the process of treating and diagnosing the prostate cancer. Traditionally, in the hospital the doctors saw the TRUS images by their eyes and manually segmented the boundary between the prostate and nonprostate. But the manually segmenting process not only needed too much time but also had different boundaries according to the doctor. To cope the problems, some automatic segmentations of the prostate have been studied to generate the constant segmentation results and get the belief from patients. In this study, we propose an average shape model to segment the prostate boundary in TRUS prostate image. The method has 3 steps. First, it finds the probe using edge distribution. Next, it finds two straight lines connected with the probe. Finally it puts the shape model to the image using the position of the probe and straight lines.