A computer-aided brain tumor diagnosis by adaptive fuzzy active contour fusion model and deep fuzzy classifier

Brain tumor classification is a significant issue in Computer-Aided Diagnosis (CAD) for clinical applications. The classification process is crucial and plays a major role to diagnosis the brain tumors. The existing works focus on recognizing brain tumors through diverse classification approaches. Though, the conventional classification approaches are suffered from high false alarm rates. To improve the early-stage brain tumor diagnosis via classification, the main intention of this paper is to introduce a novel brain tumor segmentation and classification model. The dataset gathered from the two benchmark sources is subjected to pre-processing for enhancing the quality of images, and skull stripping for extracting the region of interest from the skull. Further, a new segmentation approach termed Adaptive Fuzzy Active Contour Fusion Model (AFACFM) with a new fitness function is developed. Here, the enhancement of the segmentation is performed by the hybrid Jaya-Tunicate Swarm Algorithm (J-TSA). Next, the combination of Convolutional Neural Network (CNN) and Fuzzy classifier is performed in the final classification phase. The deep features are extracted from the pooling layer of CNN, which are subjected to the Fuzzy classifier for classifying the images into normal, benign, and malignant. As a modification, the parameters of the CNN and Fuzzy classifier are tuned by the proposed J-TSA. The comparative analysis is finally done, and this work demonstrates the potential of using deep learning in MRI images to provide a non-invasive tool for simultaneous and automated tumor segmentation and classification. Through the performance analysis, the accuracy of the designed CNN-Fuzzy using J-TSA was 77%, 29%, 19%, 8.7%, 6.8%, and 1.6% enhanced than SVM, NN, DBN, CNN, Fuzzy, and CNN-Fuzzy, respectively.

     

Performance evaluation of a region growing procedure for mammographic breast lesion identification

At present, mammography is the most effective examination for an early diagnosis of breast cancer. Nevertheless, the detection of cancer signs in mammograms is a difficult procedure owing to the great number of non-pathological structures which are also present in the image. Recent statistics show that in current breast cancer screenings 10%-25% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for Computer Aided Detection (CADe). Probably, some causes of the false-negative screening examinations are that tumoral masses have varying dimension and irregular shape, their borders are often ill-defined and their contrast is very low, thus making difficult the discrimination from parenchymal structures. Therefore, in a CADe system a preliminary segmentation procedure has to be implemented in order to separate the mass from the background tissue. In this way, various characteristics of the segmented mass can be evaluated and used in a classification step to discriminate benign and malignant cases. In this paper, we describe an effective algorithm for massive lesions segmentation based on a region-growing technique and we provide full details the performance evaluation procedure used in this specific context.     

基于卷积神经网络的胸片肺结节检测

针对目前胸片的肺结节检测方案的检出率较低,且存在大量的假阳性的问题,提出了一种新的基于卷积神经网络(CNN)的肺结节检测方案.增强肺结节区域的图像信号;选择正、负样本训练卷积神经网络模型,检测结节时用滑动窗口的方法对增强后的图片进行处理得到候选区域;根据候选区域的面积排除假阳性.方案中省略了传统方法中的肺区分割步骤,避免了因此可能丢失的肺结节图像.在日本放射技术学会(JSRT)数据库上测试结果显示,系统在平均每幅图5.0个假阳性水平下敏感度为86％,对不明显和非常不明显的结节检出率达到了84％,优于当前相关文献报道的方法.     

面向乳腺癌图像的浅层高识别卷积神经网络研究

乳腺癌是易发生且致死率高的恶性肿瘤之一,及早诊断识别是降低致死率的关键.基于应用广泛的乳腺癌病理图像,结合卷积神经网络展开乳腺癌的识别研究.针对癌症图像细节和纹理特征难以识别的问题,采用插值处理将图像进行适当放大,以便研究分析.针对卷积神经网络参数庞大不易训练和不易硬件实现的问题,提出一种精简的5卷积层W型网络结构,具...     

Classification of benign and malignant breast tumors based on hybrid level set segmentation

Computer-aided Diagnosis (CADx) technology can substantially aid in early detection and diagnosis of breast cancers. However, the overall performance of a CADx system is tied, to a large extent, to the accuracy with which the tumors can be segmented in a mammogram. This implies that the segmentation of mammograms is a critical step in the diagnosis of benign and malignant tumors. In this paper, we develop an enhanced mammography CADx system with an emphasis on the segmentation step. In particular, we present two hybrid algorithms based upon region-based, contour-based and clustering segmentation techniques to recognize benign and malignant breast tumors. In the first algorithm, in order to obtain the most accurate final segmented tumor, the initial segmented image, that is required for the level set, is provided by one of spatial fuzzy clustering (SFC), improved region growing (RG), or cellular neural network (CNN). In the second algorithm, all of the parameters which control the level set are obtained from a dynamic training procedure by the combination of both genetic algorithms (GA) and artificial neural network (ANN) or memetic algorithm (MA) and ANN. After segmenting tumors using one of the hybrid proposed methods, intensity, shape and texture features are extracted from tumors, and the appropriate features are then selected by another GA algorithm. Finally, to classify tumors as benign or malignant, different classifiers such as ANN, random forest, naïve Bayes, support vector machine (SVM), and K-nearest neighbor (KNN) are used. Experimental results confirm the efficiency of the proposed methods in terms of sensitivity, specificity, accuracy and area under ROC curve (AUC) for the classification of breast tumors. It was concluded that RG and GA in adaptive RG-LS method produce more accurate primary boundary of tumors and appropriate parameters for the level set technique in segmentation and subsequently in classification.     

基于条件能量对抗网络的肝脏和肝肿瘤分割

从图像中分割出肝脏和肝肿瘤是肝部疾病诊断重要手段之一,现有基于卷积神经网络（Convolutional Neural Network,CNN）方法通过为输入图像中每个像素分配类别标签来实现肝脏和肝肿瘤分割。CNN在对每个像素分类过程中没有使用邻域内其他像素类别信息,容易出现小目标漏检和目标边界分割模糊问题。针对这些问题,提出了条件能量对抗网络用于肝脏和肝肿瘤分割。该方法基于能量生成对抗网络（Energy-Based Generative Adversarial Network,EBGAN）和条件生成对抗网络（Conditional Generative Adversarial Network,CGAN）,使用一个基于CNN的分割网络作为生成器与一个自编码器作为判别器,通过将判别器作为一种损失函数来度量并提升分割结果与真实标注之间的相似度。在对抗训练过程中,判别器将生成器输出的分割结果作为输入并将原始图像作为条件约束,通过学习像素类别之间的高阶一致性提高分割精度,使用能量函数作为判别器避免了对抗网络训练中容易出现的梯度消失或梯度爆炸,更易于训练。在MICCAI 2017肝肿瘤分割（LiTS）挑战赛的数据集和3DIRCADb数据集上对提出的方法进行验证,实验结果表明,该方法不仅实现了肝脏与肝肿瘤的自动分割,还利用像素类别之间的高阶一致性提升了肿瘤和肝脏边界的分割精度,减少了小体积肿瘤的漏检。     

基于同一特征空间的多模态脑肿瘤分割方法

脑胶质瘤的分割依赖多种模态的核磁共振成像（MRI）的影像。基于卷积神经网络（CNN）的分割算法往往是在固定的多种模态影像上进行训练和测试,这忽略了模态数据缺失或增加问题。针对这个问题,提出了将不同模态的图像通过CNN映射到同一特征空间下并利用同一特征空间下的特征来分割肿瘤的方法。首先,不同模态的数据经过同一深度CNN提取特征;然后,将不同模态的特征连接起来,经过全连接层实现特征融合;最后,利用融合的特征实现脑肿瘤分割。模型采用BRATS2015数据集进行训练和测试,并使用Dice系数对模型进行验证。实验结果表明了所提模型能有效缓解数据缺失问题。同时,该模型较多模态联合的方法更加灵活,能够应对模态数据增加问题。     

Extraction of spiculated parts of mammogram tumors to improve accuracy of classification

Spiculated parts of masses are significant features to classify tumors in digital mammography; however, segmentation, which is used to extract the shape and contour of a tumor, eliminates them. To address this problem, the current study proposes a novel algorithm for extraction of the spiculated pixels of a tumor that are of similar intensity along a line. It first applies the sums of the differences between the central pixel and neighboring pixels in different symmetric orthogonal directions. The minimum difference between two symmetric orthogonal directions specifies the similarity of pixels in one direction as denoting a spiculated part of the mass. These parts then are added to the segmented image to enhance the shape of tumor. The features of the tumor are extracted from the final segmented image to allow its classification as benign or malignant. Simulation results showed that the accuracy and the area under the ROC curve of the proposed method for mini-MIAS and DDSM databases were 91.37% and 93.22% and 0.9776 and 0.9752, respectively. This confirms the effectiveness of the proposed algorithm for extraction of the spiculated parts of a malignant tumor with the aim of increasing the classification accuracy.

     

基于深度卷积神经网络的肺结节检测算法

在传统的肺结节检测算法中，存在检测敏感度低，假阳性数量大的问题。针对这一问题，提出了基于深度卷积神经网络（CNN）的肺结节检测算法。首先，有目的性地简化传统的全卷积分割网络；然后，创新地加入对部分CNN层的深监督并使用改进的加权损失函数，获得高质量的候选肺结节，保证高敏感度；其次，设计了基于多尺度上下文信息的三维深度CNN来增强对图像的特征提取；最后，将训练得到的融合分类模型用于候选结节分类，以达到降低假阳率的目的。所提算法使用了LUNA16数据集，并通过对比实验验证算法的性能。在检测阶段，当每个CT检测出的候选结节数为50.2时，获得的敏感度为94.3%，与传统的全卷积分割网络相比提升了4.2个百分点；在分类阶段，竞争性能指标达到0.874。实验结果表明，所提算法能够有效提高检测敏感度和降低假阳率。     

Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm

In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection – CADe) and diagnosis (Computer-Aided Diagnosis – CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean ± standard deviation value of AOM for the proposed method was 79.2 ± 8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views.     

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.     

稀疏形状先验的脑肿瘤图像分割

目的 在脑部肿瘤图像的分析过程中,准确分割出肿瘤区域对于计算机辅助脑部肿瘤疾病的诊断及治疗过程具有重要意义。然而,由于脑部图像常存在结构复杂、边界模糊、灰度不均以及肿瘤内部存在明暗区域的问题,使得肿瘤图像分割工作面临严峻挑战。为了克服上述困难,更好地实现脑部肿瘤图像分割,提出一种基于稀疏形状先验的脑肿瘤图像分割算法。方法 首先,研究脑部肿瘤图像的配准与形状描述,并以此为基础构建脑部肿瘤的稀疏形状先验约束模型;继而,将该稀疏形状先验约束模型与区域能量描述方法相结合,构建基于稀疏形状先验的能量函数;最后,对能量函数进行优化及迭代,输出脑部肿瘤区域分割结果。结果 本文使用脑胶质瘤公开数据集BraTS2017进行算法测试,本文算法的分割结果与真实数据之间的平均相似度达到93.97%,灵敏度达到91.3%,阳性预测率达到95.9%。本文算法的实验准确度较高,误判率较低,鲁棒性较强。结论 本文算法能够结合水平集方法在拓扑结构描述和稀疏表达方法在复杂形状表达方面的优势,同时由于加入了形状约束,能够有效削弱肿瘤内部明暗区域对分割结果造成的影响,从而更准确和稳定地实现脑部肿瘤图像分割。     

Patch-based Segmentation of Latent Fingerprint Images Using Convolutional Neural Network

Latent fingerprint segmentation involves marking out all the foreground regions accurately in a latent fingerprint image, but due to poor quality images and complex background, segmentation of latent fingerprint images is one of the most difficult tasks in automatic latent fingerprint recognition systems. In this article, we propose a patch-based technique for segmentation of latent fingerprint images, which uses Convolutional Neural Network (CNN) to classify patches. CNN has recently shown impressive performance in the field of pattern recognition, classification, and object detection, which inspired us to use CNN for this complex task. We trained the CNN model using SGD to classify image patches into fingerprint and non-fingerprint classes followed by proposed false patch removal technique, which uses “majority of neighbors” to remove the isolated and miss-classified patches. Finally, based on the final class of patches, an ROI is constructed to mark out the foreground from the background of latent fingerprint images. We tested our model on IIIT-D latent fingerprint database and the experimental results show improvements in the overall accuracy compared to existing methods.     

基于卷积神经网络的脑肿瘤分割方法综述

脑肿瘤自动分割对脑肿瘤诊断、手术规划和治疗评估中起着重要的作用。然而,由于脑病变结构的高可变性,组织边界模糊,以及数据有限和类不平衡等问题,导致其仍面临巨大的挑战。目前,大部分分割依赖手工,耗时耗力,易受主观影响,寻求一种高效的自动分割方法非常具有研究意义。介绍了脑肿瘤分割的研究背景、意义和难点,并概述了其发展历程;从数据和结构优化两方面详细描述基于脑肿瘤分割的卷积神经网络,简介脑分割常用的数据集和性能指标;分析了2017至2019年的BraTs挑战赛中排名靠前的算法性能,并讨论分析卷积神经网络应用于脑肿瘤分割的发展趋势。     

Deep learning based enhanced tumor segmentation approach for MR brain images

Automation in medical industry has become one of the necessities in today’s medical scenario. Radiologists/physicians need such automation techniques for accurate diagnosis and treatment planning. Automatic segmentation of tumor portion from Magnetic Resonance (MR) brain images is a challenging task. Several methodologies have been developed with an objective to enhance the segmentation efficiency of the automated system. However, there is always scope for improvement in the segmentation process of medical image analysis. In this work, deep learning-based approach is proposed for brain tumor image segmentation. The proposed method includes the concept of Stationary Wavelet Transform (SWT) and new Growing Convolution Neural Network (GCNN). The significant objective of this work is to enhance the accuracy of the conventional system. A comparative analysis with Support Vector Machine (SVM) and Convolution Neural Network (CNN) is carried out in this work. The experimental results prove that the proposed technique has outperformed SVM and CNN in terms of accuracy, PSNR, MSE and other performance parameters.     

Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization

In the medical field, image segmentation is a paramount and challenging task. The head and vertebral column make up the central nervous system (CNS), which control all the paramount functions. These include thinking, speaking, and gestures. The uncontrolled growth in the CNS can affect a person’s thinking of communication or movement. The tumor is known as the uncontrolled growth of cells in brain. The tumor can be recognized by MRI image. Brain tumor detection is mostly affected with inaccurate classification. This proposed work designed a novel classification and segmentation algorithm for the brain tumor detection. The proposed system uses the Adaptive fuzzy deep neural network with frog leap optimization to detect normality and abnormality of the image. Accurate classification is achieved with error minimization strategy through our proposed method. Then, the abnormal image is segmented using adaptive flying squirrel algorithm and the size of the tumor is detected, which is used to find out the severity of the tumor. The proposed work is implemented in the MATLAB simulation platform. The proposed work Accuracy, sensitivity, specificity, false positive rate and false negative rate are 99.6%, 99.9%, 99.8%, 0.0043 and 0.543, respectively. The detection accuracy is better in our proposed system than the existing teaching and learning based algorithm, social group algorithm and deep neural network.

     

A statistical framework for breast tumor classification from ultrasonic images

Segmentation and classification of ultrasonic breast images is extremely critical for medical diagnosis. Over the last years, various techniques have already been presented for this objective. In this paper, a proposed framework is presented to segment a given ultrasonic image with breast tumor and classify the tumor as being benign or malignant. The proposed framework depends on an active contour segmentation model to determine the tumor region, and then extract it from the ultrasonic image. After that, the Discrete Wavelet Transform (DWT) is used to extract features from the segmented images. Then, the dimensions of the resulting features are reduced by applying feature reduction approaches, namely, the Principal Component Analysis (PCA), the Linear Discriminant Analysis (LDA) and both of them together. The obtained features are submitted to a statistical classifier and the strategy of voting is used to classify the tumor. In the simulation work, 160 benign and malignant breast tumor images collected from Sirindhorn International Institute of Technology (SIIT) website are used. The average processing time for a 256 × 256 image on a laptop with Core i5, 2.3 GHz processor and 8GB RAM is 1.8 s. From the simulation results, it is found that the utilization of the PCA approach provides the best accuracy of 99.23% among the three feature reduction approaches applied. Finally, the proposed framework is compared with the Support Vector Machine (SVM) classification to evaluate its performance in terms of accuracy, sensitivity, precision, and specificity. It is noticed that the proposed framework is efficient and rapid, and it can be applied for ultrasonic breast image segmentation and classification, and thus it can assist the specialists to segment and decide whether a tumor is benign or malignant.

     

A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding

Mass detection is a very important process for breast cancer diagnosis and computer aided systems. It can be very complex when the mass is small or invisible because of dense breast tissue. Therefore, the extraction of suspicious mass region can be very challenging. This paper proposes a novel segmentation algorithm to identify mass candidate regions in mammograms. The proposed system includes three parts: breast region and pectoral muscle segmentation, image enhancement and suspicious mass regions identification. The first two parts have been examined in previous studies. In this study, we focused on suspicious mass regions identification using a combination of Havrda & Charvat entropy method and Otsu's N thresholding method. An open access Mammographic Image Analysis Society (MIAS) database, which contains 59 masses, was used for the study. The proposed system obtained a 93% sensitivity rate for suspicious mass regions identification in 56 abnormal and 40 normal images.     

Medical image segmentation based on cellular neural network

The application of cellular neural network (CNN) has made great progress in image processing. When the selected objects extraction (SOE) CNN is applied to gray scale images, its effects depend on the choice of initial points. In this paper, we take medical images as an example to analyze this limitation. Then an improved algorithm is proposed in which we can segment any gray level objects regardless of the limitation stated above. We also use the gradient information and contour detection CNN to determine the contour and ensure the veracity of segmentation effectively. Finally, we apply the improved algorithm to tumor segmentation of the human brain MR image. The experimental results show that the algorithm is practical and effective.