Blood vessel segmentation and extraction using H-minima method based on image processing techniques

Multimedia Tools and Applications - In this paper, the H-minima transform is used for blood vessel segmentation. The aim of this study is to get the high accuracy of blood vessel segmentation in...     

Automatic segmentation of cells from microscopic imagery using ellipse detection

Cell image segmentation is a necessary first step of many automated biomedical image-processing procedures. There certainly has been much research in the area. To this, a new method has been added, which automatically extracts cells from microscopic imagery, and does so in two phases. Phase 1 uses iterated thresholding to identify and mark foreground objects or `blobs' with an overall accuracy of >97%. Phase 2 of the method uses a novel genetic algorithms-based ellipse detection algorithm to identify cells, quickly and reliably. The mechanism, as a whole, has an accuracy rate >96% and takes <1 min (given our specific hardware configuration) to operate on a microscopic image     

Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform

This paper proposes a novel automatic method for the moment segmentation and peak detection analysis of heart sound (HS) pattern, with special attention to the characteristics of the envelopes of HS and considering the properties of the Hilbert transform (HT). The moment segmentation and peak location are accomplished in two steps. First, by applying the Viola integral waveform method in the time domain, the envelope (E_T) of the HS signal is obtained with an emphasis on the first heart sound (S1) and the second heart sound (S2). Then, based on the characteristics of the E_T and the properties of the HT of the convex and concave functions, a novel method, the short-time modified Hilbert transform (STMHT), is proposed to automatically locate the moment segmentation and peak points for the HS by the zero crossing points of the STMHT. A fast algorithm for calculating the STMHT of E_T can be expressed by multiplying the E_T by an equivalent window (W_E). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width of N = 1 s is validated for locating the moment segmentation and peak points for HS. The proposed moment segmentation and peak location procedure method is validated by sounds from Michigan HS database and sounds from clinical heart diseases, such as a ventricular septal defect (VSD), an aortic septal defect (ASD), Tetralogy of Fallot (TOF), rheumatic heart disease (RHD), and so on. As a result, for the sounds where S2 can be separated from S1, the average accuracies achieved for the peak of S1 (AP₁), the peak of S2 (AP₂), the moment segmentation points from S1 to S2 (AT₁₂) and the cardiac cycle (ACC) are 98.53%, 98.31% and 98.36% and 97.37%, respectively. For the sounds where S1 cannot be separated from S2, the average accuracies achieved for the peak of S1 and S2 (AP₁₂) and the cardiac cycle ACC are 100% and 96.69%.     

深度学习多部位病灶检测与分割

目的 多部位病灶具有大小各异和类型多样的特点,对其准确检测和分割具有一定的难度。为此,本文设计了一种2.5D深度卷积神经网络模型,实现对多种病灶类型的计算机断层扫描（computed tomography,CT）图像的病灶检测与分割。方法 利用密集卷积网络和双向特征金字塔网络组成的骨干网络提取图像中的多尺度和多维度信息,输入为带有标注的中央切片和提供空间信息的相邻切片共同组合而成的CT切片组。将融合空间信息的特征图送入区域建议网络并生成候选区域样本,再由多阈值级联网络组成的Cascade R-CNN（region convolutional neural networks）筛选高质量样本送入检测与分割分支进行训练。结果 本文模型在DeepLesion数据集上进行验证。结果表明,在测试集上的平均检测精度为83.15%,分割预测结果与真实标签的端点平均距离误差为1.27 mm,直径平均误差为1.69 mm,分割性能优于MULAN（multitask universal lesion analysis network for joint lesion detection,tagging and segmentation）和Auto RECIST（response evaluation criteria in solid tumors）,且推断每幅图像平均时间花费仅91.7 ms。结论 对于多种部位的CT图像,本文模型取得良好的检测与分割性能,并且预测时间花费较少,适用病变类别与DeepLesion数据集类似的CT图像实现病灶检测与分割。本文模型在一定程度上能满足医疗人员利用计算机分析多部位CT图像的需求。     

Automatic segmentation of dermoscopy images using saliency combined with adaptive thresholding based on wavelet transform

Segmentation is the essential requirement in automated computer-aided diagnosis (CAD) of skin diseases. In this paper, we propose an unsupervised skin lesion segmentation method to challenge the difficulties existing in the dermoscopy images such as low contrast, border indistinct, and skin lesion is close to the boundary. The proposed method combines the enhanced fusion saliency with adaptive thresholding based on wavelet transform to get the lesion regions. Firstly, a fusion saliency map increases the contract of the skin lesion and healthy skin, and then an adaptive thresholding method based on wavelet transform is used to obtain more accurate lesion regions. We compare the proposed method with seven state-of-the-art approaches using a series of evaluation metrics on both PH2 and ISBI2016 datasets. The results demonstrate the effectiveness of the proposed method superior to the state-of-the-art approaches in accordance with quantitative results and visual effects.

     

基于张量投票和成份标注的彩色文本的分割

自然景观中文本会受到各种噪声的污染,这给文本的分割和识别带来困难.根据不同特征值将图像分层,使用张量投票框架对每层进行分析,可以有效地去除噪声并对缺失的图像进行修补.由于要根据经验判断阈值,要达到理想的效果必须经过多次运算.引入经改进成分标注方法,确定阁值,提高了效率.实验结果表明,该方法取得了比较好的效果.     

Automatic melanoma detection and segmentation in dermoscopy images using deep RetinaNet and conditional random fields

Multimedia Tools and Applications - Melanoma is one of the major causes of death around the world and is also known as malignant skin cancer. Melanoma detection is possible at an early stage by...     

Automatic detection of microaneurysms using a novel segmentation algorithm based on deep learning techniques

Microaneurysms is the first stage of diabetic retinopathy (DR) and it plays a vital role in the computerized diagnosis. However, it is difficult to automatically detect microaneurysms in fundus images due to the complicated background and various illumination reasons. The motivation behind this, is the number of increases in diabetic patients is very large when compared with the number of ophthalmologists. The FSCA-UNET (Frequency Spatial Channel Attention UNET) segmentation model, is proposed and it is an improvement over UNET. We first use the frequency channel attention mechanism to analyze the features that were extracted from the first stage of the convolution layer, and we obtain good results. Then, we included a spatial attention map with frequency attention, also known as FSCA-UNET, which makes use of inter-spatial connections between features. Our deep neural model with an encoder-decoder structure termed FSCA-UNET produced more accurate results. Our novel algorithm outdated the performance measures of the existing segmentation algorithms. The proposed segmentation algorithm was trained and tested on Indian Diabetic Retinopathy Image Dataset (IDRiD), and E-ophtha Dataset and we got promising results in terms of sensitivity, specificity, dice coefficient, precision, F1 score, and accuracy.     

Automatic detection of targets against cluttered backgrounds using a fractal-oriented statistical analysis and Radon transform

The paper concerns a two-stage method of local and global information processing used to extract low contrast, linear features embedded in SAR images degraded by speckle. The first processing stage which involves the scaling laws of fractal geometry identifies regions in images where there is a high probability of a target feature. Such features cause deviations from the statistics for a background only. Thus by computing these deviations, the statistics of the target features are automatically separated from those of the background. The result of this processing is then passed, in the form of a binary image, to the second stage where it is parametrically transformed and the threshold transformation is used to deduce the location of the target feature within the image. The method is robust, automatic and can be shown to be computationally realistic. It is illustrated using data from a SAR image (C-band, VV polarisation).     

Automatic image segmentation using constraint learning and propagation

In this paper, we propose automatic image segmentation using constraint learning and propagation. Recently, kernel learning is receiving much attention because a learned kernel can fit the given data better than a predefined kernel. To effectively learn the constraints generated by initial seeds for image segmentation, we employ kernel propagation (KP) based on kernel learning. The key idea of KP is first to learn a small-sized seed-kernel matrix and then propagate it into a large-sized full-kernel matrix. By applying KP to automatic image segmentation, we design a novel segmentation method to achieve high performance. First, we generate pairwise constraints, i.e., must-link and cannot-link, from initially selected seeds to make the seed-kernel matrix. To select the optimal initial seeds, we utilize global k-means clustering (GKM) and self-tuning spectral clustering (SSC). Next, we propagate the seed-kernel matrix into the full-kernel matrix of the entire image, and thus image segmentation results are obtained. We test our method on the Berkeley segmentation database, and the experimental results demonstrate that the proposed method is very effective in automatic image segmentation.     

Skin lesion segmentation using k-mean and optimized fire fly algorithm

Multimedia Tools and Applications - Digital image processing is turning out to be increasingly more significant in the health care field used to diagnose skin cancer. The death rate is increasing...     

混合分水岭变换和改进FCM的图像分割方法

分水岭变换是图像分割的一种强有力的形态工具,能够自动生成一系列封闭分割区域。其不足之处是过分割、对噪声敏感。为克服分水岭变换固有的缺点,综合利用非线性滤波和改进的FCM算法优化分水岭变换得出的初始分割,提出了一种新的混合分割算法-HWIF（Hybrid Watershed and Improved FCM）分割法。与MeanShift算法及区域合并算法相比,该方法充分利用了区域的灰度和区域间的空间信息。实验结果表明该算法能有效克服分水岭算法的过分割问题,且分割效果优于以上两种方法。     

基于多任务学习的肝脏肿瘤自动分割方法

针对深度学习医学影像分割所需的标注数据获取困难且数量少的问题,提出一种基于多任务学习的肝脏肿瘤自动分割方法.提出该方法的主要动机一方面是想要缓解分割数据样本少的问题,另一方面是想要利用不同任务之间有用的信息来提升整体的学习效果.该方法所采用数据集一部分是逐像素的分割数据集,一部分是形式为bounding-box的目标检测数据集.该方法首先通过共享主网络挖掘不同任务之间的相关性,提取通用的特征,然后两个子任务分支通过注意力机制从共享主网络上提取对自身有用的特征,最后两个子网络结合自身的特异性特征与主网络上提取的通用特征来分别完成检测与分割任务.实验结果表明,在一定的样本比例下,该方法相较于U-Net的Dice系数提升了6.67个百分点,表明该方法能够有效利用额外的目标检测数据来提高分割任务的精度.     

A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms

Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach.     

迭代分水岭和脊检测的图像分割

对具有边界不明显和亮度非均匀特征的图像,如金相学图像,使用传统方法进行分割难以得到令人满意的分割效果.因此,提出了一种新的图像分割算法--基于迭代过程的分水岭改进算法.该算法运用脊来限定区域生长的范围,采用双阈值方法选择种子,而脊叠加为分水岭算法中的最高吃水线.为了解决过分割的问题,利用贝叶斯分类规则反复地进行区域块的合并.实验结果表明,在无需调整太多参数的情况下,该算法可以有效地实现图像分割.     

Automatic MR image segmentation using maximization of mutual information

Microsystem Technologies - Magnetic resonance (MR) brain image segmentation is an important task for the early detection of any deformation followed by the quantitative analysis for the prediction...     

RBF神经网络和阈值分割实现视网膜硬性渗出自动检测

为自动检测出眼底图像中的硬性渗出,构建眼底图像的糖尿病视网膜病变自动筛查系统,提出一种基于RBF神经网络和阈值分割的硬性渗出自动检测方法.首先,利用基于最小类内离散度的改进Otsu分割方法对眼底图像绿色通道进行粗分割获取病灶候选区域;然后,利用logistic回归对候选区域的多个特征进行选择;最后,利用候选区域的优化特征集及相应判定结果建立RBF神经网络;此外,提出采用后处理以进一步提高检测精度.利用本文方法对50幅不同颜色、不同亮度的眼底图像进行硬性渗出自动检测,得到图像水平灵敏度100％,特异性90.9％,准确率96.0％;病灶区域水平灵敏度93.9％,阳性预测值95.5％;平均每幅图像处理时间13.6 s.结果表明本文方法稳定可靠,能快速有效地自动检测出眼底图像中的硬性渗出.     

Automatic segmentation of acoustic musical signals using hiddenMarkov models

In this paper, we address an important step toward our goal of automatic musical accompaniment-the segmentation problem. Given a score to a piece of monophonic music and a sampled recording of a performance of that score, we attempt to segment the data into a sequence of contiguous regions corresponding to the notes and rests in the score. Within the framework of a hidden Markov model, we model our prior knowledge, perform unsupervised learning of the data model parameters, and compute the segmentation that globally minimizes the posterior expected number of segmentation errors. We also show how to produce “online” estimates of score position. We present examples of our experimental results, and readers are encouraged to access actual sound data we have made available from these experiments     

Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images

Automatic liver segmentation is difficult because of the wide range of human variations in the shapes of the liver. In addition, nearby organs and tissues have similar intensity distributions to the liver, making the liver's boundaries ambiguous. In this study, we propose a fast and accurate liver segmentation method from contrast-enhanced computed tomography (CT) images. We apply the two-step seeded region growing (SRG) onto level-set speed images to define an approximate initial liver boundary. The first SRG efficiently divides a CT image into a set of discrete objects based on the gradient information and connectivity. The second SRG detects the objects belonging to the liver based on a 2.5-dimensional shape propagation, which models the segmented liver boundary of the slice immediately above or below the current slice by points being narrow-band, or local maxima of distance from the boundary. With such optimal estimation of the initial liver boundary, our method decreases the computation time by minimizing level-set propagation, which converges at the optimal position within a fixed iteration number. We utilize level-set speed images that have been generally used for level-set propagation to detect the initial liver boundary with the additional help of computationally inexpensive steps, which improves computational efficiency. Finally, a rolling ball algorithm is applied to refine the liver boundary more accurately. Our method was validated on 20 sets of abdominal CT scans and the results were compared with the manually segmented result. The average absolute volume error was 1.25+/-0.70%. The average processing time for segmenting one slice was 3.35 s, which is over 15 times faster than manual segmentation or the previously proposed technique. Our method could be used for liver transplantation planning, which requires a fast and accurate measurement of liver volume.