Outlining the prostate boundary using the harmonics method

In a computerised ultrasound image guidance for automated prostatectomy system, it is necessary to identify a smooth, continuous contour for the prostate (boundary) from the ultrasound image. The radial bas-relief (RBR) method, which has been reported previously, can extract a skeletonised image from an ultrasound image automatically. After this process the prostate boundary is clearly revealed. However, analysis of the image is far from complete, as there are many spurious branches that create too much ambiguity for the system to define the actual boundary. There are also sections missing from the prostate boundary. Therefore further post-processing is required to describe and define the prostate boundary. In the paper, the harmonics method is used to describe the prostate boundary. The harmonics method uses Fourier information for noise removal and encodes a smooth boundary. The results of using the harmonics method after application of the RBR method on ultrasound images are presented. Factors that affect the performance are also highlighted and discussed.     

Boundary delineation in transrectal ultrasound image for prostate cancer

This paper presents a new advanced automatic edge delineation model for the detection and diagnosis of prostate cancer on transrectal ultrasound (TRUS) images. The proposed model is to improve prostate boundary detection system by modifying a set of preprocessing algorithms including tree-structured nonlinear filter (TSF), directional wavelet transforms (DWT) and tree-structured wavelet transform (TSWT). The model consists of a preprocessing module and a segmentation module. The preprocessing module is implemented for noise suppression, image smoothing and boundary enhancement. The active contours model is used in the segmentation module for prostate boundary detection in two-dimensional (2D) TRUS images. Experimental results show that the addition of the preprocessing module improves the accuracy and sensitivity of the segmentation module, compared to the implementation of the segmentation module alone. It is believed that the proposed automatic boundary detection module for the TRUS images is a promising approach, which provides an efficient and robust detection and diagnosis strategy and acts as "second opinion" for the physician's interpretation of prostate cancer.     

Left-ventricle boundary detection from nuclear medicine images

We present here a new algorithm for segmentation of nuclear medicine images to detect the left-ventricle (LV) boundary. In this article, other image segmentation techniques, such as edge detection and region growing, are also compared and evaluated. In the edge detection approach, we explored the relationship between the LV boundary characteristics in nuclear medicine images and their radial orientations: we observed that no single brightness function (eg, maximum of first or second derivative) is sufficient to identify the boundary in every direction. In the region growing approach, several criteria, including intensity change, gradient magnitude change, gradient direction change, and running mean differences, were tested. We found that none of these criteria alone was sufficient to successfully detect the LV boundary. Then we proposed a simple but successful region growing method—Contour-Modified Region Growing (CMRG). CMRG is an easy-to-use, robust, and rapid image segmentation procedure. Based on our experiments, this method seems to perform quite well in comparison to other automated methods that we have tested because of its ability to handle the problems of both low signal-to-noise ratios (SNR) as well as low image contrast without any assumptions about the shape of the left ventricle.     

Segmentation of the prostate from suprapubic ultrasound images

We present a technique for semiautomated segmentation of human prostates using suprapubic ultrasound (US) images. In this approach, a speckle reducing anisotropic diffusion (SRAD) is applied to enhance the images and the instantaneous coefficient of variation (ICOV) is utilized for edge detection. Segmentation is accomplished via a parametric active contour model in a polar coordinate system that is tailored to the application. The algorithm initially approximates the prostate boundary in two stages. First a primary contour is detected using an elliptical model, followed by a primary contour optimization using an area-weighted mean-difference binary flow geometric snake model. The algorithm was assessed by comparing the computer-derived contours with contours produced manually by three sonographers. The proposed method has application in radiation therapy planning and delivery, as well as in automated volume measurements for ultrasonic diagnosis. The average root mean square discrepancy between computed and manual outlines is less than the inter-observer variability. Furthermore, 76% of the computer-outlined contour is less than 1 sigma manual outline variance away from "true" boundary of prostate. We conclude that the methods developed herein possess acceptable agreement with manually contoured prostate boundaries and that they are potentially valuable tools for radiotherapy treatment planning and verification.     

Semiautomatic three-dimensional segmentation of the prostate using two-dimensional ultrasound images

In this paper, we report on two methods for semiautomatic three-dimensional (3-D) prostate boundary segmentation using 2-D ultrasound images. For each method, a 3-D ultrasound prostate image was sliced into the series of contiguous 2-D images, either in a parallel manner, with a uniform slice spacing of 1 mm, or in a rotational manner, about an axis approximately through the center of the prostate, with a uniform angular spacing of 5 degrees. The segmentation process was initiated by manually placing four points on the boundary of a selected slice, from which an initial prostate boundary was determined. This initial boundary was refined using the Discrete Dynamic Contour until it fit the actual prostate boundary. The remaining slices were then segmented by iteratively propagating this result to an adjacent slice and repeating the refinement, pausing the process when necessary to manually edit the boundary. The two methods were tested with six 3-D prostate images. The results showed that the parallel and rotational methods had mean editing rates of 20% and 14%, and mean (mean absolute) volume errors of -5.4% (6.5%) and -1.7% (3.1%), respectively. Based on these results, as well as the relative difficulty in editing, we conclude that the rotational segmentation method is superior.     

基于视觉感光层功能的菌落图像多强度边缘检测研究

借鉴视觉神经系统在轮廓感知中的独特优势,提出一种基于视觉感光层功能的图像边缘检测新方法。构建以带漏感的积累发放（LIF）神经元电生理模型为基本单元的神经元网络;根据特定时间窗口内各个神经元的脉冲发放情况,对神经元的增强(ON)或抑制(OFF)类别进行判断;通过拮抗式感受野特性以及神经元激励的反馈增强模式,实现弱边缘的凸显;为克服视觉感光层所具有的适应性并凸显弱细节的对比度,对图像进行多方向、多距离尺度的移动,并融合感光层神经元网络脉冲发放率的差异信息,最后实现图像边缘的有效检测。以具有丰富边缘特性的20幅菌落图像为样本,以边缘置信度和重构相似度作为评价指标,对多强度边缘进行检测。结果表明,所提出方法可以有效完整地检测出图像多强度边缘,且其对弱边缘检测的重构相似度均值高于08,检测准确性有显著的提高(P<005)。所提出的利用生理视觉系统特性进行边缘检测,为包含多强度边缘信息的图像处理提供崭新的思路。     

Automatic detection of linear artifacts in medical images

Our purpose in this study is to describe an algorithm for the automatic detection of linear artifacts in medical images. Linear artifacts arise as a result of many different forms of tissues and tissue boundaries within the imaging volume. Additionally, linear artifacts can arise for artificial structures such as radioactive seeds and radioactive linear sources. It is the purpose of the described algorithm to automatically detect linear artifacts of a certain length and diameter. The algorithm was written and compiled on a Pentium-4 based computer in the Microsoft Visual C/C++ language. Inert coils supplied by Radiomed Inc. were implanted into a standard prostate ultrasound phantom. Transaxial ultrasound images of the implanted phantom were obtained at 2 mm increments. The coded algorithm was then applied to the ultrasound imaging volume to automatically segment out the implanted coils. Thirteen coils were implanted in the prostate phantom. Thirteen coils were automatically identified in the imaging volume. An algorithm was developed to automatically determine the position and orientation of radioactive coils within an imaging volume. The algorithm successfully identified thirteen coils implanted in an ultrasound prostate phantom.     

Development of a fully automatic scheme for detection of masses in whole breast ultrasound images

Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.     

基于区域生长超声图像分割血管边界反映血管病变

背景：通过分析超声血管图像能反映血管的病变情况。 目的：采用区域生长理论对超声图像进行图像分割,分析边界点的相对位移。 方法：先对视频图像分帧,将动态图像转换为静态图像,采用Gabor滤波、自适应直方图量化去除超声图像噪声,然后运用区域生长法对图像做分割,接着通过开闭运算、sobel算子检测图像边界,最后提取出两条血管边界。 结果与结论：通过Gabor滤波、区域生长法等手段,得到了比较好的分割结果。区域生长法在处理速度上满足了实时性要求,具有一定的通用性。并且通过分析边界点的相对位移曲线,一定程度上反映血管的病变。     

Novel Genetic-Neuro-Fuzzy Filter for Speckle Reduction from Sonography Images

Edge-preserving speckle noise reduction is essential to computer-aided ultrasound image processing and understanding. A new class of genetic-neuro-fuzzy filter is proposed to optimize the trade-off between speckle noise removal and edge preservation. The proposed approach combines the advantages of the fuzzy, neural, and genetic paradigms. Neuro-fuzzy approaches are very promising for nonlinear filtering of noisy images. Fuzzy reasoning embedded into the network structure aims at reducing errors while fine details are being processed. The learning method based on the real-time genetic algorithms (GAs) performs an effective training of the network from a collection of training data and yields satisfactory results after a few generations.The performance of the proposed filter has been compared with that of the commonly used median and Wiener filters in reducing speckle noises on ultrasound images. We evaluate this filter by passing the filters output to the edge detection algorithm and observing its ability to detect edge pixels.Experimental results show that the proposed genetic-neuro-fuzzy technique is very effective in speckle noise reduction as well as detail preserving even in the presence of highly noise corrupted data, and it works significantly better than other well-known conventional methods in the literature.     

Comparative evaluation of image segmentation methods for volume quantitation in SPECT.

A wide variety of image segmentation techniques have been proposed for the measurement of organ or lesion volumes in SPECT images. Evaluation of the relative performance of the various methods is difficult due to wide variations in system response characteristics, size, shape, and contrast of the imaged objects, and image acquisition and processing techniques. Selected image segmentation methods for volume quantitation in SPECT were applied to a set of simulated SPECT images containing objects ranging in volume from 1.8 to 113.1 cc. The specific segmentation methods included: (1) operator drawn regions of interest, (2) count-based methods, (3) three levels of fixed thresholds, (4) an adaptive threshold (GLH method), (5) a two-dimensional (2-D) edge detection method, and (6) a three-dimensional (3-D) edge detection method. In general, the 3-D edge detection method required minimal operator intervention while providing the most accurate and consistent estimates of object volume across changes in object contrast and size.     

A three-dimensional deformable model for segmentation of human prostate from ultrasound images.

Segmentation of human prostate from ultrasound (US) images is a crucial step in radiation therapy, especially in real-time planning for US image-guided prostate seed implant. This step is critical to determine the radioactive seed placement and to ensure the adequate dose coverage of prostate. However, due to the low contrast of prostate and very low signal-to-noise ratio in US images, this task remains as an obstacle. The manual segmentation of this object is time consuming and highly subjective. In this work, we have proposed a three-dimensional (3D) deformable surface model for automatic segmentation of prostate. The model has a discrete structure made from a set of vertices in the 3D space that form triangle facets. The model converges from an initial shape to its equilibrium iteratively, by a weighted sum of the internal and external forces. Internal forces are based on the local curvature of the surface and external forces are extracted from the volumetric image data by applying an appropriate edge filter. We have also developed a method for initialization of the model from a few initial contours that are drawn on different slices. During the deformation, a resampling procedure is used to maintain the resolution of the model. The entire model is applied in a multiscale scheme, which increases the robustness and speed, and guarantees a better convergence. The model is tested on real clinical data and initial results are very promising.     

基于改进TransUnet网络的血管内超声图像边界提取方法研究

血管内超声(IVUS)图像是观测血管内部结构的首选影像学手段,基于IVUS图像的内膜和中外膜边界的提取是实现冠脉粥样硬化精准诊断的前提和关键。针对IVUS图像结构复杂、对比度低、边界提取困难等问题,本研究提出一种基于改进TransUnet网络的分割方法。首先,针对IVUS图像边界提取难点,对边血管、血管分叉、导丝伪像、阴影等4种图像结构进行建模,并基于建模结果予以定向数据增强;而后,结合IVUS图像的环状结构分布特点,在TransUnet网络中提出了Polar-bias归纳偏置的策略,并对IVUS图像进行像素级的分类;最后,基于分类结果优化GVF snake模型的外力场,进而提取IVUS图像的内膜和中外膜边界。采用国际标准IVUS图像数据集(两组不同中心频率,共512幅图像)对算法进行评测,引入JMard距离(JM),Hausdorff距离(HD)和面积差异百分比(PAD)等3个评测指标,在数据集A中JM为0.87, HD为0.87, PAD为0.18,数据集B中JM为0.91, HD为0.25, PAD为0.08。实验结果表明,所提出的算法在两组数据集的内膜及中外膜提取问题中的表现均...     

Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis

In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.     

基于活动轮廓模型和边缘对比度特征量的血管内超声图像边缘提取

血管内超声（IVUS）图像冠状动脉血管壁的边缘提取对冠状动脉疾病的诊断和治疗有着重要意义。本研究提出了一种用于自动提取IVUS序列图像冠状动脉血管壁内、外膜边缘的方法。该方法基于活动轮廓模型以及本研究所定义的边缘对比度特征量，利用Hopfield神经网络并结合模拟退火算法自动提取IVUS序列图像冠状动脉血管壁的内、外膜边缘。实验结果表明，本研究方法易于实现，而且准确性和可靠性较高，对IVUS序列图像处理的可重复性和鲁棒性较好，是一种较好的全局最优化算法。     

医学图像中模糊边界的提取

本文提出了一种基于模糊数字拓扑的方法来实现医学图象中模糊对象的提取,该方法根据所给定的物体和边缘特征信息将原始医学图象转换到模糊连通空间,获得模糊边界,对复杂形状的对象还提出了模糊边界逐步改进和优化的算法,最终可以跟踪出满足实际需要的连通、平滑及优化的物体轮廓线,算法在超声图象中取得了满意的结果。     

Seed-based transrectal ultrasound-fluoroscopy registration method for intraoperative dosimetry analysis of prostate brachytherapy

Prostate brachytherapy is an effective treatment option for early-stage prostate cancer. During a prostate brachytherapy procedure, transrectal ultrasound (TRUS) and fluoroscopy imaging modalities complement each other by providing good visualization of soft tissue and implanted seeds, respectively. Therefore, the registration of these two imaging modalities, which are readily available in the operating room, could facilitate intraoperative dosimetry, thus enabling physicians to implant additional seeds into the underdosed portions of the prostate while the patient is still on the operating table. It is desirable to register TRUS and fluoroscopy images by using the seeds as fiducial markers. Although the locations of all the implanted seeds can be reconstructed from three fluoroscopy images, only a fraction of these seeds can be located in TRUS images. It is challenging to register the TRUS and fluoroscopy images by using the identified seeds, since the correspondence between them is unknown. Furthermore, misdetection of nonseed structures as seeds can lead to the inclusion of spurious points in the data set. We developed a new method called iterative optimal assignment (IOA) to overcome these challenges in TRUS-fluoroscopy registration. By using the Hungarian method in an optimization framework, IOA computes a set of transformation parameters that yield the one-to-one correspondence with minimum cost. We have evaluated our registration method at varying noise levels, seed detection rates, and number of spurious points using data collected from 25 patients. We have found that IOA can perform registration with an average root mean square error of about 0.2 cm even when the seed detection rate is only 10%. We believe that IOA can offer a robust solution to seed-based TRUS-fluoroscopy registration, thus making intraoperative dosimetry possible.     

Automated prostate recognition: a key process for clinically effective robotic prostatectomy

Clinical trials of PROBOT, a robotic system for prostate surgery, have shown that robotic surgery of soft tissue can be successful. Monitoring of the progress of the resection has shown to be a necessary feature of an effective robotic system for prostate surgery. It should provide the surgeon with a reliable method of assessing the cavity during resection. An automatic system for intraoperative monitoring of the progress of the resection during robotic prostatectomy consists of two subsystems: real-time intraoperative imaging of the prostate and automatic identification of the contour of the gland on each image. The development of a fully automatic scheme for prostate recognition on transurethral ultrasound scans is reported. A genetic algorithm has been developed to automatically adjust a model of the prostate boundary until an optimum fit to the prostate in a given image is obtained. An analysis of its performance on 22 different ultrasound images showed an average error of 6.21 mm. Use of a genetic algorithm and a constrained prostate model have shown to be a robust way to automatically identify the prostate in ultrasound images. The scheme is able to produce approximate prostate boundaries, without any human intervention, on ultrasound scans of varying quality. In addition to soft tissue robotic surgery, the genetic algorithm technique is also applicable to a wide range of computer assisted surgical techniques.     

Automated prostate recognition: a key process for clinically effective robotic prostatectomy

Clinical trials of PROBOT, a robotic system for prostate surgery, have shown that robotic surgery of soft tissue can be successful. Monitoring of the progress of the resection has shown to be a necessary feature of an effective robotic system for prostate surgery. It should provide the surgeon with a reliable method of assessing the cavity during resection. An automatic system for intraoperative monitoring of the progress of the resection during robotic prostatectomy consists of two subsystems: real-time intraoperative imaging of the prostate and automatic identification of the contour of the gland on each image. The development of a fully automatic scheme for prostate recognition on transurethral ultrasound scans is reported. A genetic algorithm has been developed to automatically adjust a model of the prostate boundary until an optimum fit to the prostate in a given image is obtained. An analysis of its performance on 22 different ultrasound images showed an average error of 6.21 mm. Use of a genetic algorithm and a constrained prostate model have shown to be a robust way to automatically identify the prostate in ultrasound images. The scheme is able to produce approximate prostate boundaries, without any human intervention, on ultrasound scans of varying quality. In addition to soft tissue robotic surgery, the genetic algorithm technique is also applicable to a wide range of computer assisted surgical techniques.