Tissue segmentation in ultrasound images by using genetic algorithms

This paper presents a genetic based incremental neural network (GINeN) for the segmentation of tissues in ultrasound images. Performances of the GINeN and the Kohonen network are investigated for tissue segmentation in ultrasound images. Feature extraction is carried out by using continuous wavelet transform. Pixel intensities at the same spatial location on 12 wavelet planes and on the original image are considered as features, leading to 13-dimensional feature vectors. The same training set is used for the training of the Kohonen network and the GINeN.

This paper proposes the use of wavelet transform and genetic based incremental neural network together in order to increase the segmentation performance. It is observed that genetic based incremental neural network gives satisfactory segmentation performance for ultrasound images.     

基于改进RCE和RBF神经网络的静态手势识别

针对手势识别的手区域分割、手势特征提取和手势分类的三个过程,提出了一种新的静态手势识别方法。改进了传统的RCE神经网络用于手区域的分割,具有更高的运行速度和更强的抗噪能力。依Freeman链码方向提取手的边缘到掌心的距离作为手势的特征向量。将上一步得到的手势特征向量作为RBF神经网络的输入,进行网络的训练和分类。实验验证了该方法的有效性和可行性,并用其实现了人和仿人机器人的剪刀石头布的猜拳游戏。     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.     

Neural network classification of homomorphic segmented heart sounds

A novel method for segmentation of heart sounds (HSs) into single cardiac cycle (S₁-Systole-S₂-Diastole) using homomorphic filtering and K-means clustering is presented. Feature vectors were formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. These feature vectors were then used as input to the neural networks. Grow and Learn (GAL) and Multilayer perceptron-Backpropagation (MLP-BP) neural networks were used for classification of three different HSs (Normal, Systolic murmur and Diastolic murmur). It was observed that the classification performance of GAL was similar to MLP-BP. However, the training and testing times of GAL were lower as compared to MLP-BP. The proposed framework could be a potential solution for automatic analysis of HSs that may be implemented in real time for classification of HSs.     

An incremental neural network for tissue segmentation in ultrasound images

This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.     

Hand image segmentation using color and RCE neural network

This paper presents a color segmentation method based on RCE neural network for hand image segmentation in the gesture-based human–service robot interaction system. The study on skin color distributions in different color spaces indicates that skin colors cluster in a small region in a color space. The RCE neural network characterizes the skin color distribution region using skin color prototypes together with their spherical influence fields during training stage, and identifies the skin regions in the color image during running stage. Experimental results have demonstrated the effectiveness of this method for the segmentation of various hand images as well as general color images with complex backgrounds.     

Segmentation of MR and CT Images by Using a Quantiser Neural Network

A Quantiser Neural Network (QNN) is proposed for the segmentation of MR and CT images. Elements of a feature vector are formed by image intensities at one neighbourhood of the pixel of interest. QNN is a novel neural network structure, which is trained by genetic algorithms. Each node in the first layer of the QNN forms a hyperplane (HP) in the input space. There is a constraint on the HPs in a QNN. The HP is represented by only one parameter in d-dimensional input space. Genetic algorithms are used to find the optimum values of the parameters which represent these nodes. The novel neural network is comparatively examined with a multilayer perceptron and a Kohonen network for the segmentation of MR and CT head images. It is observed that the QNN gives the best classification performance with fewer nodes after a short training time.     

基于特征级融合神经网络的磁共振成像前列腺肿瘤CAD模型

针对磁共振成像(MRI)前列腺肿瘤感兴趣区域(ROI)在高维特征表示下存在特征相关和维数灾难问题,提出了一种基于主成分分析(PCA)的特征级融合神经网络(NN)的MRI前列腺肿瘤CAD模型。首先提取MRI前列腺肿瘤ROI的6维几何特征、6维统计特征、7维Hu不变矩特征、56维灰度共生矩阵的纹理特征、3维Tamura纹理特征和24维频域特征,得到102维特征矢量;然后通过PCA进行特征级融合得到累计贡献率达到89.62%的8维变换特征,降低特征矢量的维数;再次利用经典的神经网络(四种训练算法BFGS拟牛顿算法、BP算法、最速梯度下降算法和Levenberg-Marquardt算法)作为分类器进行分类识别;最后以180幅前列腺患者的MRI图像为原始数据,采用基于特征级融合神经网络(NN)的计算机辅助诊断模型对前列腺肿瘤进行辅助诊断。实验结果表明:经过特征级融合的神经网络识别前列腺良恶性肿瘤的能力至少提高10%左右,这种特征级融合策略是有效的,一定程度上提高了特征之间的不相关性。     

Segmentation of MR and CT Images Using a Hybrid Neural Network Trained by Genetic Algorithms

A novel hybrid neural network trained by the genetic algorithms is presented. Genetic algorithms are used to improve the neural net's classification performance while minimizing the number of nodes. Each node of the network forms a closed region in the input space. The closed regions, which are formed by the nodes, intersect each other. The performance of the proposed hybrid neural network is compared with the multilayer perceptron, and the restricted Coulomb energy network for the segmentation of MR and CT head images. Experimental results show that the proposed neural network gives the best classification performance with a small number of nodes in short training times.     

ECG beat classification by a novel hybrid neural network

This paper presents a novel hybrid neural network structure for the classification of the electrocardiogram (ECG) beats. Two feature extraction methods: Fourier and wavelet analyses for ECG beat classification are comparatively investigated in eight-dimensional feature space. ECG features are determined by dynamic programming according to the divergence value. Classification performance, training time and the number of nodes of the multi-layer perceptron (MLP), restricted Coulomb energy (RCE) and a novel hybrid neural network are comparatively presented. In order to increase the classification performance and to decrease the number of nodes, the novel hybrid structure is trained by the genetic algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 96% by using the hybrid structure.     

深层聚合残差密集网络的超声图像左心室分割

目的 超声图像是临床医学中应用最广泛的医学图像之一,但左心室超声图像一般具有强噪声、弱边缘和组织结构复杂等问题,其图像分割难度较大。临床上需要一种效率高、质量好的超声图像左心室分割算法。本文提出一种基于深层聚合残差密集网络（deep layer aggregation for residual dense network,DLA-RDNet）的超声图像左心室分割算法。方法 对获取的超声图像进行形态学操作,定位目标区域,得到目标图像。构建残差密集网络（residual dense network,RDNet）用于提取图像特征,并将RDNet得到的层次信息通过深层聚合（deep layer aggregation,DLA）的方式紧密融合到一起,得到分割网络DLA-RDNet,用于实现对超声图像左心室的精确分割。通过深监督（deep supervision,DS）方式为网络剪枝,简化网络结构,提升网络运行速度。结果 数据测试集的实验结果表明,所提算法平均准确率为95.68%,平均交并比为97.13%,平均相似性系数为97.15%,平均垂直距离为0.31 mm,分割轮廓合格率为99.32%。与6种分割算法相比,所提算法的分割精度更高。在测试阶段,每幅图像仅需不到1 s的时间即可完成分割,远远超出了专业医生的分割速度。结论 提出了一种深层聚合残差密集神经网络对超声图像左心室进行分割,通过主、客观对比实验表明本文算法的有效性,能够较对比方法更实时准确地对超声图像左心室进行分割,符合临床医学中超声图像左心室分割的需求。     

Learning lateral interactions for feature binding and sensory segmentation from prototypic basis interactions

We present a hybrid learning method bridging the fields of recurrent neural networks, unsupervised Hebbian learning, vector quantization, and supervised learning to implement a sophisticated image and feature segmentation architecture. This architecture is based on the competitive layer model (CLM), a dynamic feature binding model, which is applicable on a wide range of perceptual grouping and segmentation problems. A predefined target segmentation can be achieved as attractor states of this linear threshold recurrent network, if the lateral weights are chosen by Hebbian learning. The weight matrix is given by the correlation matrix of special pattern vectors with a structure dependent on the target labeling. Generalization is achieved by applying vector quantization on pair-wise feature relations, like proximity and similarity, defined by external knowledge. We show the successful application of the method to a number of artificial test examples and a medical image segmentation problem of fluorescence microscope cell images.     

Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images

Region of interest (ROI) is a region used to extract features. In breast ultrasound (BUS) image, the ROI is a breast tumor region. Because of poor image quality (low SNR (signal/noise ratio), low contrast, blurry boundaries, etc.), it is difficult to segment the BUS image accurately and produce a ROI which precisely covers the tumor region. Due to the requirement of accurate ROI for feature extraction, fully automatic classification of BUS images becomes a difficult task. In this paper, a novel fully automatic classification method for BUS images is proposed which can be divided into two steps: “ROI generation step” and “ROI classification step”. The ROI generation step focuses on finding a credible ROI instead of finding the precise tumor location. The ROI classification step employs a novel feature extraction and classification strategy. First, some points in the ROI are selected as the “classification checkpoints” which are evenly distributed in the ROI, and the local texture features around each classification checkpoint are extracted. For each ROI, all the classification checkpoints are classified. Finally, the class of the BUS image is determined by analyzing every classification checkpoint in the corresponding ROI. Both steps were implemented by utilizing a supervised texture classification approach. The experiments demonstrate that the proposed method is very robust to the segmentation of BUS images, and very effective and useful for classifying breast tumors.     

Seg-CapNet:A Capsule-Based Neural Network for the Segmentation of Left Ventricle from Cardiac Magnetic Resonance Imaging

Deep neural networks (DNNs) have been extensively studied in medical image segmentation.However,existing DNNs often need to train shape models for each object to be segmented,which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI).In this paper,we propose a capsule-based neural network,named Seg-CapNet,to model multiple regions simultaneously within a single training process.The Seg-CapNet model consists of the encoder and the decoder.The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers,capsule layers,and fully-connected layers.And the decoder transforms the feature vectors into segmentation masks by up-sampling.Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers,which are conducive to the backpropagation of the model.The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture,as well as semantic features including the position and size of the objects,which is beneficial for improving the segmentation accuracy.The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge.Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7％ and the average Hausdorff distance is reduced by 22％.The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions.     

Construction and verification of retinal vessel segmentation algorithm for color fundus image under BP neural network model

To improve the accuracy of retinal vessel segmentation, a retinal vessel segmentation algorithm for color fundus images based on back-propagation (BP) neural network is proposed according to the characteristics of retinal blood vessels. Four kinds of green channel image enhancement results of adaptive histogram equalization, morphological processing, Gaussian matched filtering, and Hessian matrix filtering are used to form feature vectors. The BP neural network is input to segment blood vessels. Experiments on the color fundus image libraries DRIVE and STARE show that this algorithm can obtain complete retinal blood vessel segmentation as well as connected vessel stems and terminals. When segmenting most small blood vessels, the average accuracy on the DRIVE library reaches 0.9477, and the average accuracy on the STARE library reaches 0.9498, which has a good segmentation effect. Through verification, the algorithm is feasible and effective for blood vessel segmentation of color fundus images and can detect more capillaries.

     

Seg-CapNet:A Capsule-Based Neural Network for the Segmentation of Left Ventricle from Cardiac Magnetic Resonance Imaging

Deep neural networks (DNNs) have been extensively studied in medical image segmentation.However,existing DNNs often need to train shape models for each object to be segmented,which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI).In this paper,we propose a capsule-based neural network,named Seg-CapNet,to model multiple regions simultaneously within a single training process.The Seg-CapNet model consists of the encoder and the decoder.The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers,capsule layers,and fully-connected layers.And the decoder transforms the feature vectors into segmentation masks by up-sampling.Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers,which are conducive to the backpropagation of the model.The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture,as well as semantic features including the position and size of the objects,which is beneficial for improving the segmentation accuracy.The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge.Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7％ and the average Hausdorff distance is reduced by 22％.The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions.     

Application of InP Neural Network to ECG BeatClassification

This paper presents an application of a hybrid neural network structure to the classification of the electrocardiogram (ECG) beats. Three different feature extraction methods are comparatively examined: discrete cosine transform, wavelet transform and a direct method. Classification performances, training times and the numbers of nodes of Kohonen network, Restricted Coulomb Energy (RCE) network and the hybrid neural network are presented. To increase the classification performance and to decrease the number of nodes, the hybrid neural network is trained by Genetic Algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 98% by using the hybrid neural network structure and discrete cosine transform together.     

Robust information gain based fuzzy c-means clustering and classification of carotid artery ultrasound images

In this paper, a robust method is proposed for segmentation of medical images by exploiting the concept of information gain. Medical images contain inherent noise due to imaging equipment, operating environment and patient movement during image acquisition. A robust medical image segmentation technique is thus inevitable for accurate results in subsequent stages. The clustering technique proposed in this work updates fuzzy membership values and cluster centroids based on information gain computed from the local neighborhood of a pixel. The proposed approach is less sensitive to noise and produces homogeneous clustering. Experiments are performed on medical and non-medical images and results are compared with state of the art segmentation approaches. Analysis of visual and quantitative results verifies that the proposed approach outperforms other techniques both on noisy and noise free images. Furthermore, the proposed technique is used to segment a dataset of 300 real carotid artery ultrasound images. A decision system for plaque detection in the carotid artery is then proposed. Intima media thickness (IMT) is measured from the segmented images produced by the proposed approach. A feature vector based on IMT values is constructed for making decision about the presence of plaque in carotid artery using probabilistic neural network (PNN). The proposed decision system detects plaque in carotid artery images with high accuracy. Finally, effect of the proposed segmentation technique has also been investigated on classification of carotid artery ultrasound images.     

Medical image segmentation using a contextual-constraint-based Hopfield neural cube

Neural-network-based image techniques such as the Hopfield neural networks have been proposed as an alternative approach for image segmentation and have demonstrated benefits over traditional algorithms. However, due to its architecture limitation, image segmentation using traditional Hopfield neural networks results in the same function as thresholding of image histograms. With this technique high-level contextual information cannot be incorporated into the segmentation procedure. As a result, although the traditional Hopfield neural network was capable of segmenting noiseless images, it lacks the capability of noise robustness. In this paper, an innovative Hopfield neural network, called contextual-constraint-based Hopfield neural cube (CCBHNC) is proposed for image segmentation. The CCBHNC uses a three-dimensional architecture with pixel classification implemented on its third dimension. With the three-dimensional architecture, the network is capable of taking into account each pixel's feature and its surrounding contextual information. Besides the network architecture, the CCBHNC also differs from the original Hopfield neural network in that a competitive winner-take-all mechanism is imposed in the evolution of the network. The winner-take-all mechanism adeptly precludes the necessity of determining the values for the weighting factors for the hard constraints in the energy function in maintaining feasible results. The proposed CCBHNC approach for image segmentation has been compared with two existing methods. The simulation results indicate that CCBHNC can produce more continuous, and smoother images in comparison with the other methods.     

Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains

Due to the complicated structure of breast and poor quality of ultrasound images, accurately and automatically locating regions of interest (ROIs) and segmenting tumors are challenging problems for breast ultrasound (BUS) computer-aided diagnosis systems. In this paper, we propose a fully automatic BUS image segmentation approach for performing accurate and robust ROI generation, and tumor segmentation. In the ROI generation step, the proposed adaptive reference point (RP) generation algorithm can produce the RPs automatically based on the breast anatomy; and the multipath search algorithm generates the seeds accurately and fast. In the tumor segmentation step, we propose a segmentation framework in which the cost function is defined in terms of tumor?s boundary and region information in both frequency and space domains. First, the frequency constraint is built based on the newly proposed edge detector which is invariant to contrast and brightness; and then the tumor pose, position and intensity distribution are modeled to constrain the segmentation in the spatial domain. The well-designed cost function is graph-representable and its global optimum can be found. The proposed fully automatic segmentation method is applied to a BUS database with 184 cases (93 benign and 91 malignant), and the performance is evaluated by the area and boundary error metrics. Compared with the newly published fully automatic method, the proposed method is more accurate and robust in segmenting BUS images.