基于改进区域生长算法的肝脏管道图像分割方法

在图像分割过程中,传统区域生长法种子点的选取需人工判定,工作量较大,效率较低。为了减少种子点选取时的用户交互量,本文提出了一种基于种子点位置预判的改进区域生长算法。该算法基于血管骨架线具有代表性的特点,通过坐标系的映射转换来预判图像中肝脏管道的位置,减少了人工参与,实现了图像种子点数目和位置的自动确定。肝脏图像分割实验结果表明,该算法在较少的用户交互情况下,实现了序列图像的种子点位置预判,获得了较满意的图像分割效果。     

基于Fast Marching Method的肝脏CT图像序列自动分割

快速行进法(fast marching method,FMM)已被证明在图像分割方面具有优势,在此基础上提出了一个混合分割的算法。这个方法加入了图像分割后处理步骤,成功解决了活体肝脏CT系列图像自动分割问题。首先是通过滤波去噪等处理得到速率系数图像,然后根据CT图像相邻层间的相似特点计算FMM所需的参数进行图像分割,最后使用开运算修正肝脏边缘。整个序列分割过程只需用户定义一个种子点,减少了人工干预,从而提高了效率和准确性。     

基于改进的Snake模型分割肝脏的CT图像

Snake(主动轮廓线)模型,具有良好的获取特定区域内目标边缘的能力,是一种极为有效的图像分割方法,利用围绕目标心形的圆环内平均灰度差异来确定初始轮廓点,对噪声的干扰有一定的抑制作用,并减少了人工选取的工作量,将离散Snake算法与分段DP算法有效结合来获取肝脏CT图像的特征边缘点,以提高Snake算法的收敛速度.最后利用单调性原则对肝脏CT图像的边缘点进行分区,在各个单调区间内采用曲线拟合的方法来获得连续的肝脏CT图像边缘,最后用Roipoly函数,由这些坐标显示原输入图像,得到BW的二值图像,最终可以显示完整的CT值没有改变的肝脏图像.     

Automated Classification of Liver Disorders using Ultrasound Images

This paper presents a novel approach for detection of Fatty liver disease (FLD) and Heterogeneous liver using textural analysis of liver ultrasound images. The proposed system is able to automatically assign a representative region of interest (ROI) in a liver ultrasound which is subsequently used for diagnosis. This ROI is analyzed using Wavelet Packet Transform (WPT) and a number of statistical features are obtained. A multi-class linear support vector machine (SVM) is then used for classification. The proposed system gives an overall accuracy of ~95% which clearly illustrates the efficacy of the system.     

A Robust Method for Ventriculomegaly Detection from Neonatal Brain Ultrasound Images

Ventriculomegaly is the most commonly detected abnormality in neonatal brain. It can be defined as a condition when the human brain ventricle system becomes dilated. This in turn increases the intracranial pressure inside the skull resulting in progressive enlargement of the head. Sometimes it may also cause mental disability or death. For these reasons early detection of ventriculomegaly has become an important task. In order to identify ventriculomegaly from neonatal brain ultrasound images, we propose an automated image processing based approach that measures the anterior horn width as the distance between medial wall and floor of the lateral ventricle at the widest point. Measurement is done in the plane of the scan at the level of the intraventricular foramina. Our study is based on neonatal brain ultrasound images in the midline coronal view. In addition to ventriculomegaly detection, this work also includes both cross sectional and longitudinal study of anterior horn width of lateral ventricles. Experiments were carried out on brain ultrasound images of 96 neonates with gestational age ranging from 26 to 39 weeks and results have been verified with the ground truth provided by doctors. Accuracy of the proposed scheme is quite promising.     

Near-set Based Mucin Segmentation in Histopathology Images for Detecting Mucinous Carcinoma

This paper introducesnear-set based segmentation method for extraction and quantification of mucin regions for detecting mucinouscarcinoma (MC which is a sub type of Invasive ductal carcinoma (IDC)). From histology point of view, the presence of mucin is one of the indicators for detection of this carcinoma. In order to detect MC, the proposed method majorly includes pre-processing by colour correction, colour transformation followed by near-set based segmentation and post-processing for delineating only mucin regions from the histological images at 40×. The segmentation step works in two phases such as Learn and Run.In pre-processing step, white balance method is used for colour correction of microscopic images (RGB format). These images are transformed into HSI (Hue, Saturation, and Intensity) colour space and H-plane is extracted in order to get better visual separation of the different histological regions (background, mucin and tissue regions). Thereafter, histogram in H-plane is optimally partitioned to find set representation for each of the regions. In Learn phase, features of typical mucin pixel and unlabeled pixels are learnt in terms of coverage of observed sets in the sample space surrounding the pixel under consideration. On the other hand, in Run phase the unlabeled pixels are clustered as mucin and non-mucin based on its indiscernibilty with ideal mucin, i.e. their feature values differ within a tolerance limit. This experiment is performed for grade-I and grade-II of MC and hence percentage of average segmentation accuracy is achieved within confidence interval of [97.36 97.70] for extracting mucin areas. In addition, computation of percentage of mucin present in a histological image is provided for understanding the alteration of such diagnostic indicator in MC detection.     

A Hybrid Method Based on Fuzzy Clustering and Local Region-Based Level Set for Segmentation of Inhomogeneous Medical Images

Reliable sources report that errors in drug administration are increasing the number of harmed or killed inpatients, during healthcare. This development is in contradiction to patient safety norms. A correctly designed hospital-wide ubiquitous system, using advanced inpatient identification and matching techniques, should provide correct medicine and dosage at the right time. Researchers are still making grouping proof protocol proposals based on the EPC Global Class 1 Generation 2 ver. 1.2 standard tags, for drug administration. Analyses show that such protocols make medication unsecure and hence fail to guarantee inpatient safety. Thus, the original goal of patient safety still remains. In this paper, a very recent proposal (EKATE) upgraded by a cryptographic function is shown to fall short of expectations. Then, an alternative proposal IMS-NFC which uses a more suitable and newer technology; namely Near Field Communication (NFC), is described. The proposed protocol has the additional support of stronger security primitives and it is compliant to ISO communication and security standards. Unlike previous works, the proposal is a complete ubiquitous system that guarantees full patient safety; and it is based on off-the-shelf, new technology products available in every corner of the world. To prove the claims the performance, cost, security and scope of IMS-NFC are compared with previous proposals. Evaluation shows that the proposed system has stronger security, increased patient safety and equal efficiency, at little extra cost.     

Inter-Greedy Technique for Fusion of Different Segmentation Strategies Leading to High-Performance Carotid IMT Measurement in Ultrasound Images

User-based estimation of intima-media thickness (IMT) of carotid arteries leads to subjectivity in its decision support systems, while being used as a cardiovascular risk marker. During automated computer-based decision support, we had developed segmentation strategies that follow three main courses of our contributions: (a) signal processing approach combined with snakes and fuzzy K-means (CULEXsa), (b) integrated approach based on seed and line detection followed by probability based connectivity and classification (CALEXsa), and (c) morphological approach with watershed transform and fitting (WS). These grayscale segmentation algorithms yielding carotid wall boundaries has certain bias along with their own merits. We recently developed a fusion technique that was helpful in removing bias which combines two carotid wall boundaries using ground truth as an ideal marker. Here we have extended this fusion concept by taking merits of these multiple boundaries, so called, Inter-Greedy (IG) approach. Further we estimate IMT from these fused boundaries from multiple sources. Starting from the technique with the overall least system error (the snake-based one), we iteratively swapped the vertices of the profiles until we minimized its overall distance with respect to ground truth. The fusion boundary was the Inter-Greedy boundary. We used the polyline distance metric for performance evaluation and error minimization. We ran the segmentation protocol over the database of 200 carotid longitudinal B-mode ultrasound images and compared the performance of all the four techniques (CALEXia, CULEXsa, WS, IG). The mean error of Inter-Greedy technique yielded 0.32 ± 0.44 pixel (20.0 ± 27.5 μm) for the LI boundary (a 33.3% ± 5.6% improvement over initial best performing technique) and 0.21 ± 0.34 pixel (13.1 ± 21.3 μm) for MA boundary (a 32.3% ± 6.7% improvement). IMT measurement error for Greedy method was 0.74 ± 0.75 pixel (46.3 ± 46.9 μm), a 43.5% ± 2.4% improvement.     

An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep

We present an automated method for segmentation of epithelial cells in images taken from ThinPrep scenes by a digital camera in a cytology lab. The method covers both steps of localization of cell objects in low resolution and detection of cytoplasm and nucleus boundary in high resolution. The underlying method makes use of geometric active contours as a powerful tool of segmentation. We also provide the analysis of the connected cells. For this purpose an automatic circular decomposition method is incorporated and adapted to the application by changing its segmentation condition. The results are evaluated numerically and compared with those of previous work in literature.     

A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images

Day by day, huge amount of information is collected in medical databases. These databases include quite interesting information that could be exploited in diagnosis of illnesses and medical treatment of patients. Classification of these data is getting harder as the databases are expanded. On the other hand, automated image analysis and processing is one of the most promising areas of computer vision used in medical diagnosis and treatment. In this context, retinal fundus images, offering very high resolutions that are sufficient for most of the clinical cases, provide many indications that could be exploited in diagnosing and screening retinal degenerations or diseases. Consequently, there is a strong demand in developing automated evaluation systems to utilize the information stored in the medical databases. This study proposes an automatic method for segmentation of ARMD in retinal fundus images. The method used in the automated system extracts lesions of the ARMD by employing a statistical method. In order to do this, the statistical segmentation method is first used to extract the healthy area of the macula that is more familiar and regular than the unhealthy parts. Here, characteristic images of the patterns of the macula are extracted and used to segment the healthy textures of an eye. In addition to this, blood vessels are also extracted and then classified as healthy regions. Finally, the inverse image of the segmented image is generated which determines the unhealthy regions of the macula. The performance of the method is examined on various quality retinal fundus images. Segmented images are also compared with consecutive images of the same patient to follow up the changes in the disease.     

基于马尔可夫随机场的肝实质分割

介绍了马尔可夫随机场（markov random field,MRF）的基本理论,以及基于MRF的图像分割模型及其求解过程。利用MRF分割方法对肝脏CT图片进行了分割,实验结果表明：该方法能够有效对肝脏实质进行分割,在一些模糊区域有更好的分割效果,可用于CT图像序列中的肝实质自动分割。     

Unsupervised Fuzzy Based Vessel Segmentation In Pathological Digital Fundus Images

Performing the segmentation of vasculature in the retinal images having pathology is a challenging problem. This paper presents a novel approach for automated segmentation of the vasculature in retinal images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in color fundus images. Matched filtering is utilized to enhance the contrast of blood vessels against the background. The enhanced blood vessels are then segmented by employing spatially weighted fuzzy c-means clustering based thresholding which can well maintain the spatial structure of the vascular tree segments. The proposed method’s performance is evaluated on publicly available DRIVE and STARE databases of manually labeled images. On the DRIVE and STARE databases, it achieves an area under the receiver operating characteristic curve of 0.9518 and 0.9602 respectively, being superior to those presented by state-of-the-art unsupervised approaches and comparable to those obtained with the supervised methods.     

手腕骨三维图像分割方法

为了对手腕骨进行运动学分析并对骨折手术固定辅助设计,本文提出一种对手腕骨的三维分割方法,即采用基于空间位置的方法将手腕骨独立分开,以便独立研究各部分在不同情况下的运动与受力。该方法将手腕8块腕骨分割开来,并能独立显示控制测量。为手腕骨疾病的诊断治疗提供,新的技术方法。     

一种基于BP网络的CT图像肝实质分割算法

提出一种基于BP网络分割CT图像序列中肝实质的方法。首先选取训练样本，提取样本图像中肝脏的纹理特征，作为输入向量，以对训练样本手工分割的结果作为导师信号，对BP神经网络进行训练，再用训练好的网络对CT图像序列中的肝实质进行分割，最后对分割后的结果进行三维区域生长及孔洞填充处理。实验结果表明：该方法能够有效的对肝脏纹理特征明显的CT图像序列进行分割，可用于CT图像序列的自动分割。     

基于MITK的脑核磁共振图像的分割与三维重建

本文尝试在微机上使用Visual C++开发环境结合MITK平台对人体头部MRI图像进行感兴趣区域的图像分割与三维重建,为下一步开发基于微机的医学影像三维可视化系统打下基础.     

Noninvasive Diagnostic Technique for Nonalcoholic Fatty Liver Disease Based on Features of Tongue Images

Objective: To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease(NAFLD) based on features of tongue images. Methods: Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, ...     

基于卷积神经网络和超像素的CT图像肝脏分割

肝癌是威胁人类健康的重大疾病之一。从医学影像中将肝脏组织准确地分割出来,是计算机辅助肝脏疾病诊断与手术规划中一个重要环节。由于肝脏的个体差异,周围器官的灰度值相似等因素,从CT图像中精准分割肝脏存在一定困难。提出一种结合卷积神经网络和超像素的CT图像肝脏自动分割方法。首先利用卷积神经网络进行目标检测,自动定位肝脏区域,再利用超像素算法对肝脏进行分割,最后进行腐蚀、膨胀、中值滤波等后处理。本文采用3DIRCADb公开数据集对提出的肝脏自动分割算法进行评估和验证,结果表明肝脏自动分割的DICE指标为0.951,VOE指标为0.0917,RVD指标为-0.018,显示出较好的分割精度。     

低对比度医学图像全自动分割算法

由于医学图像的对比度较低以及各种组织器官的边缘往往较为模糊,医学图像的分割是医学图像处理中的一个经典难题。如果能将各种分割对象的先验信息加入到分割算法中,将会改善分割效果。针对CT图像中的前列腺器官分割问题,利用水平集函数获得初始分割轮廓,结合从手工分割图像中获得的形状和纹理先验信息,采用遗传算法来演化分割轮廓。仿真实验结果证明该方法能有效地分割出低对比度的医学器官。     

基于种子区域增长的胆管分割方法研究

目的：对CT片中的胆管进行分割。方法：采用图像分割技术中的种子区域增长法,首先,选取种子点,得到种子点所对应CT值和相邻26个点均值,然后以种子点所在位置为起点往其临近26点扩散。若其临近点CT值与种子点CT值差值在正负10个HU或者均值满足相同要求则该点为同类点,依次类推,直到不再有同类点时算法结束。结果：自动分割的准确率〉80%,计算误差〈10%。结论：基于种子区域增长的分割方法能自动地有效地对胆管进行分割。     

DWT-Based Segmentation Method for Coronary Arteries

This work presents a new method for segmenting coronary arteries automatically in computed tomography angiography (CTA) data sets. The method automatically isolates heart and coronary arteries from surrounding structures and search for the probable location of coronary arteries by 3D region growing. Based on the dilation of the probable location, discrete wavelet transformation (DWT) and λ?–?mean operation complete accurate detection of coronary arties. Finally, the proposed method is tested on clinical CTA data-sets. The results on clinical datasets show that the proposed method is able to extract each branch of arteries when comparing to commercial software GE Healthcare and delineated ground truth.