• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.6
• /
• pp.865-871
• /
• 2022

In this paper, we propose a method of extracting prostate region using morphological characteristics of ultra-sonic image of prostate. In the first step of the proposed method, the edge area of the prostate image is extracted. The histogram of ultra-sonic image is used to extract base objects to detect the upper edge of prostate region by altering the contrast of the image, then, the lower edges of the extracted base objects are connected by using monotone cubic spline interpolation to extract the upper edge. Step 2, Otsu's binarization is applied to the region under the extracted upper edge of the prostate ultra-sonic image to extract the lower edge of prostate. In the last step, the upper and the lower edges are connected to extract prostate region and by comparing the extracted region of prostate with the one measured manually, the result showed that the morphological characteristics of prostate in ultrasonic image can be utilized to extract the prostate region.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.603-611
• /
• 2016

Generally, the doctors manually delineated the prostate boundary seeing the image by their eyes, but the manual method not only needed quite much time but also had different boundaries depending on doctors. To reduce the effort like them the automatic delineating methods are needed, but detecting the boundary is hard to do since there are lots of uncertain textures or speckle noises. There have been studied in SVM, SIFT, Gabor texture filter, snake-like contour, and average-shape model methods. Besides, there were lots of studies about 2 and 3 dimension images and CT and MRI. But no studies have been developed superior to human experts and they need additional studies. For this, this paper proposes a method that delineates the boundary predicting its texture features and its average distribution on the prostate image. As result, we got the similar boundary as the method of human experts.

• Journal of Digital Contents Society
• /
• v.16 no.5
• /
• pp.717-725
• /
• 2015

Prostate images have been used in the diagnosis of prostate using TRUS images being relatively cheap. Ultrasound images are recorded with 3 dimension and one diagnostic exam is made with a number of the images. A doctor can see 2 dimensional images on the monitor sequentially and 3 dimensional ones to diagnose a disease. To display the images, 2-d images are used with raw 2-d ones, but 3-d images need to be segmented by the prostates and their backgrounds to be seen from different angles and with cut images of inner side. Especially on detecting the boundary, the ones in the middle of all images are easy to find the boundary but the base and apex of the images are hard to do it since there are lots of uncertain boundary. So, in this paper we propose the method that applies an average shape model and detects the boundary, and shows its superiority compared to the existing methods with experiments.

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

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

• Journal of Digital Contents Society
• /
• v.15 no.6
• /
• pp.675-682
• /
• 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
• /
• v.1 no.3
• /
• pp.187-194
• /
• 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 Korea Multimedia Society
• /
• v.14 no.3
• /
• pp.348-355
• /
• 2011

In this paper, we propose an automatic prostate segmentation method from dynamic magnetic resonance (MR) images. Our method detects contrast-enhanced images among the dynamic MR images using an average intensity analysis. Then, the candidate regions of prostate are detected by the B-spline non-rigid registration and subtraction between the pre-contrast and contrast-enhanced MR images. Finally, the prostate is segmented by performing a dilation operation outward, and sequential shape propagation inward. Our method was validated by ten data sets and the results were compared with the manually segmented results. The average volumetric overlap error was 6.8%, and average absolute volumetric measurement error was 2.5%. Our method could be used for the computer-aided prostate diagnosis, which requires an accurate prostate segmentation.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.9
• /
• pp.695-699
• /
• 2009

In this paper, we propose an automatic prostate segmentation technique using image intensity and gradient information. Our method is composed of four steps. First, rays at regular intervals are generated. To minimize the effect of noise, the start and end positions of the ray are calculated. Second, the profiles on each ray are sorted based on the gradient. And priorities are applied to the sorted gradient in the profile. Third, boundary points are extracted by using gradient priority and intensity distribution. Finally, to reduce the error, the extracted boundary points are corrected by using B-spline interpolation. For accuracy evaluation, the average distance differences and overlapping region ratio between results of manual and automatic segmentations are calculated. As the experimental results, the average distance difference error and standard deviation were 1.09mm $\pm0.20mm$. And the overlapping region ratio was 92%.

• Journal of the Korean Society of Radiology
• /
• v.16 no.6
• /
• pp.779-786
• /
• 2022

This study is designed to examine the effects of Dioscorea Quinqueloba extract as a natural radiation protection agent on the prostate and heart of male rats. Dioscorea Quinqueloba extract is well known to prevent the male-specific disease and heart disease. In this study, the Gamma-ray 10 Gy was irradiated in whole body of male rat to identify radioprotective effect by Dioscorea Quinqueloba extract. After irradiation, tissue change, SOD (Superoxide Dismutase) activity changes and hematological changes were observed. DQ+IR group showed higher lymphocyte, white blood cell, platelet levels than the IR group. In the NC and DQ groups, the number of prostate gland cells and the gap between cells were relatively narrow. But in the IR group, the cells died significantly and the gap widened. In the DQ+IR group, the gap between cells increased similarly to the IR group, but the number of dead cells was noticeably smaller. In the NC and DQ groups, the cardiovascular and myocardium are clearly separated, and cell nuclei are in good condition. But in the IR group, the cardiovascular and myocardium boundaries were disrupted, and the number of dead cell nuclei was high. In the DQ+IR group, although the boundaries were widened, but not disrupted and the number of dead cell nuclei was high. Therefore, Dioscorea Quinqueloba extract is judged to have radioprotective properties for the prostate and cardiovascular.