Method for Narrow Vascular Structure Segmentation in Ct Data.

International audienc

A Pc-Based Freehand Three-Dimensional Ultrasound.

Breast cancer is the number one killer disease among women in Malaysia. In the diagnosis of breast cancer, breast ultrasound examination is commonly used as a supplement to mammography

ANALYSIS OF GALL-BLADDER IMAGES BY USING STATIONARY WAVELET TRANSFORM AND DISCRETE WAVELET TRANSFORM

Ayrıt sezimi algoritmaları biyomedikal görüntü analizinde önemli algoritmalardır. Bu çalışmada ayrıt sezimi için, histogram eşleme, ayrık dalgacık dönüşümü (ADD) ve durağan dalgacık dönüşümü (DDD) yöntemleri, safra kesesi ses üstü imgelerinin kalitesini iyileştirmede kullanılmıştır. Ayrıca, ortanca süzgeçleme algoritması, bu tekniklerden sonra görüntü üzerine uygulanmıştır. Sonuçta bu algoritmaların başarımı, görüntü entropi, parçalı t-testi ve CPU zamanı gibi çeşitli başarım ölçütleri kulllanılarak karşılaştırılmıştır. The edge detection algorithms are important in biomedical image analysis. In this work histogram equalization, the discrete wavelet transform and the stationary wavelet transform techniques were used to improve the quality of the gall bladder ultrasonic images for edge detection. Also the median filtering algorithm was used after applying the both techniques. Then the performances of these algorithms were compared by several performance measures such as image entropy, paired t-test, and CPU time

Automatic Prostate Segmentation in Ultrasound Images using Gradient Vector Flow Active Contour

ABSTRACT: Prostate cancer is one of the leading causes of death by cancer among men in the world. Ultrasonography is said to be the safest technique in medical imaging so it is used extensively in prostate cancer detection. On the other hand, determining of prostate's boundary in TRUS (Transrectal Ultrasound) images is very necessary in lots of treatment methods prostate cancer. So the first and essential step for computer aided diagnosis (CAD) is the automatic prostate segmentation that is an open problem yet. But the low SNR, presence of strong speckle noise, Weak edges and shadow artifacts in these kinds of images limit the effectiveness of classical segmentation schemes. The classical segmentation methods fail completely or require post processing step to remove invalid object boundaries in the segmentation results. This paper has proposed a fully automatic algorithm for prostate segmentation in TRUS images that overcomes the explained problems completely. The presented algorithm contains three main stages. First, morphological smoothing and stick's filter are used for noise removing. A neural network is employed in the second step to find a point in prostate region. Finally in the last step, the prostate boundaries are extracted by GVF active contour. Some experiments for the performance validity of the presented method, compared with the extracted prostate by the proposed algorithm with manually-delineated boundaries by radiologist. The results show that our method extracts prostate boundaries with mean square area error lower than 4.4%

Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to investigate the viability of level set image segmentation methods for the detection of corpora lutea (corpus luteum, CL) boundaries in ultrasonographic ovarian images. It was hypothesized that bovine CL boundaries could be located within 1–2 mm by a level set image segmentation methodology.</p> <p>Methods</p> <p>Level set methods embed a 2D contour in a 3D surface and evolve that surface over time according to an image-dependent speed function. A speed function suitable for segmentation of CL's in ovarian ultrasound images was developed. An initial contour was manually placed and contour evolution was allowed to proceed until the rate of change of the area was sufficiently small. The method was tested on ovarian ultrasonographic images (<it>n </it>= 8) obtained <it>ex situ</it>. A expert in ovarian ultrasound interpretation delineated CL boundaries manually to serve as a "ground truth". Accuracy of the level set segmentation algorithm was determined by comparing semi-automatically determined contours with ground truth contours using the mean absolute difference (MAD), root mean squared difference (RMSD), Hausdorff distance (HD), sensitivity, and specificity metrics.</p> <p>Results and discussion</p> <p>The mean MAD was 0.87 mm (sigma = 0.36 mm), RMSD was 1.1 mm (sigma = 0.47 mm), and HD was 3.4 mm (sigma = 2.0 mm) indicating that, on average, boundaries were accurate within 1–2 mm, however, deviations in excess of 3 mm from the ground truth were observed indicating under- or over-expansion of the contour. Mean sensitivity and specificity were 0.814 (sigma = 0.171) and 0.990 (sigma = 0.00786), respectively, indicating that CLs were consistently undersegmented but rarely did the contour interior include pixels that were judged by the human expert not to be part of the CL. It was observed that in localities where gradient magnitudes within the CL were strong due to high contrast speckle, contour expansion stopped too early.</p> <p>Conclusion</p> <p>The hypothesis that level set segmentation can be accurate to within 1–2 mm on average was supported, although there can be some greater deviation. The method was robust to boundary leakage as evidenced by the high specificity. It was concluded that the technique is promising and that a suitable data set of human ovarian images should be obtained to conduct further studies.</p

X-Ray Image Processing and Visualization for Remote Assistance of Airport Luggage Screeners

X-ray technology is widely used for airport luggage inspection nowadays. However, the ever-increasing sophistication of threat-concealment measures and types of threats, together with the natural complexity, inherent to the content of each individual luggage make x-ray raw images obtained directly from inspection systems unsuitable to clearly show various luggage and threat items, particularly low-density objects, which poses a great challenge for airport screeners. This thesis presents efforts spent in improving the rate of threat detection using image processing and visualization technologies. The principles of x-ray imaging for airport luggage inspection and the characteristics of single-energy and dual-energy x-ray data are first introduced. The image processing and visualization algorithms, selected and proposed for improving single energy and dual energy x-ray images, are then presented in four categories: (1) gray-level enhancement, (2) image segmentation, (3) pseudo coloring, and (4) image fusion. The major contributions of this research include identification of optimum combinations of common segmentation and enhancement methods, HSI based color-coding approaches and dual-energy image fusion algorithms —spatial information-based and wavelet-based image fusions. Experimental results generated with these image processing and visualization algorithms are shown and compared. Objective image quality measures are also explored in an effort to reduce the overhead of human subjective assessments and to provide more reliable evaluation results. Two application software are developed − an x-ray image processing application (XIP) and a wireless tablet PC-based remote supervision system (RSS). In XIP, we implemented in a user-friendly GUI the preceding image processing and visualization algorithms. In RSS, we ported available image processing and visualization methods to a wireless mobile supervisory station for screener assistance and supervision. Quantitative and on-site qualitative evaluations for various processed and fused x-ray luggage images demonstrate that using the proposed algorithms of image processing and visualization constitutes an effective and feasible means for improving airport luggage inspection