Mass lesion detection with a fuzzy neural network

This paper presents a novel fuzzy neural network (FNN) approach to detect malignant mass lesions on mammograms. The FNN is a self-adjusting and adaptive system. It is simple in structure and easy to incorporate experts’ knowledge and fuzzified factors in the detection of malignant mass lesions on mammograms. The FNN has four layers. The first layer is the input layer consisting of 4 fuzzy neurons. The second layer has 4 ordinary neurons. The third layer consists of N maximum fuzzy neurons. The number of fuzzy neurons, N, in the third layer is determined during the training process and varies with the network parameters and data distribution. The fourth layer has 2 maximum fuzzy neurons and one competitive fuzzy neuron. Mammograms were obtained from the digital database for screening mammography, DDSM. Six-hundred and seventy regions of interest (ROIs) were extracted from 100 mammograms. All extracted ROIs were randomly divided into two sets: training and testing sets. The co-occurrence matrix of each ROI was computed. Textural features were calculated at sizes of 256×256 and 768×768, respectively. The feature differences at these two image sizes were computed for each feature. These feature differences are very discriminant in differentiating between malignant masses and normal tissues regardless of lesion shape, size, and subtlety. After training, the FNN can correctly detect all malignant masses on mammograms in the testing group. The true-positive fraction (TPF) is 0.92 when the number of false positives (FP) is 1.33 per mammogram and 1.0 when the FP is 2.15 per mammogram. The proposed approach will be very useful for breast cancer control.     

基于PSVM的主动学习肿块检测方法

肿块区域通常形态各异、差异性较大,并且与正常组织相比没有明显的区别,严重影响了肿块自动检测系统的性能.为了能够有效地提高乳腺X线图像中肿块的检测灵敏度,通过引入包含了样本间相互制约关系的具有成对约束的SVM (PSVM)算法,提出了一种基于PSVM 的主动学习机制.其中,由系统根据样本的不确定性和相互之间的特征匹配距离,主动选择应该反馈给训练集的成对样本.实验结果表明,这种基于PSVM的主动学习方法,能够充分利用样本所包含的信息,使得检测方法具有更好的推广能力和检测性能.     

RETRACTED ARTICLE: An improved data mining technique for classification and detection of breast cancer from mammograms

The high incidence of breast cancer in women has increased significantly in the recent years. Mammogram breast X-ray imaging is considered the most effective, low-cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesion exist, only 15–30% of masses referred for surgical biopsy are actually malignant. Physician experience of detecting breast cancer can be assisted by using some computerized feature extraction and classification algorithms. Computer-aided classification system was used to help in diagnosing abnormalities faster than traditional screening program without the drawback attribute to human factors. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256 × 256 pixels size. The second step is the feature extraction; we used a set of 26 features, and we found that these features are capable of differentiating between normal and cancerous breast tissues in order to minimize the classification error. The third step is the classification process; we used the technique of the association rule mining to classify between normal and cancerous tissues. The proposed system was shown to have the large potential for cancer detection from digital mammograms.

     

基于Kmean和ELM的乳腺肿块检测方法

肿块是乳腺癌在X线图像上的一个主要表现。提出了一种肿块自动检测算法。该方法包括四个步骤：在图像预处理阶段,去除背景、标记、胸肌和噪声,图像分割和图像增强;利用Kmean方法找到感兴趣区域（ROI）;提取能够表征肿块的特征;利用极限学习机（Extreme Learning Machine,ELM）分类器去除假阳性,将图像中的肿块和非肿块分离开来。通过对MIAS数据库中乳腺X线图像的测试实验,得到的检测肿块的准确率为93.5%。     

改进U型残差网络用于肺结节检测

针对计算机断层扫描（CT）影像中肺结节检测灵敏度较低,且存在大量假阳性的问题,提出一种改进的U型残差网络用于肺结节检测。采取U-net网络的U型结构并利用残差学习方式构建深层次网络,同时引入自校正卷积增加特征的信息提取能力,进行通道间与局部信息增强,有利于检测不同形态的结节;通过引入的通道注意力机制,对特征提取过程中的特征进行重标定,实现自适应学习特征权重,进一步提高检测的准确率;引入DR loss作为该算法的分类损失函数,用于解决数据正负样本失衡问题。在LUNA16数据集对所提算法进行了验证,CPM得分达到0.901,提高了肺结节检测的灵敏度,而且有效降低了检测结果的平均假阳性个数,可有效辅助放射科医师对肺结节进行检测。     

基于灰度共生矩阵的图像纹理特征提取分析

图像纹理作为一种重要的视觉手段,是图像中普遍存在而又难以描述的特征。目前常用的纹理特征提取的方法主要有统计方法、模型方法、信号处理方法和结构方法。灰度共生矩阵即为灰度级的空间相关矩阵,以其为基础的统计方法通过对矩阵统计量的求取较好地提取到了纹理特征,通过选取关键参数编程并进行仿真实现,分别求取了四个方向的灰度共生矩阵及其特征量来分析图像的纹理特征。     

一种多特征分类识别算法融合的网络钓鱼识别技术

针对页面特征提取实时性差的问题进行了研究,提出将特征分类,并行提取、检测、再融合结果的方法。首先提取三个类别的主要特征,包括文本、视觉和网络链接;然后,分别利用了贝叶斯算法、EMD算法以及网络爬虫来进行分类;并且基于后验概率来确定权值的最终选取。最后,把这三个分类结果进行融合。通过对贝叶斯、加权和加权贝叶斯的比较,从正确率、漏报率和误报率对算法进行评估,实验表明采用加权贝叶斯的方法来进行融合计算效果最佳,能够提供较高的准确率和较低的误报和漏报,提高检测的精度和实时性。     

A new approach for oil tank detection using deep learning features with control false alarm rate in high-resolution satellite imagery

ABSTRACT

Oil tank detection is a challenging task, primarily due to high time-consumption. This paper aims at further investigating this challenge and proposes a new hierarchical approach to detect oil tanks, especially with respect to how false alarm rates are reduced. The proposed approach is divided into four stages: region of interest (ROI) extraction, circular object detection, feature extraction, and classification. The first stage, which is a key component of this approach to reduce false alarm and processing time, is applied by an improved faster region-based convolutional neural network (Faster R-CNN) to extract oil depots. In the second stage, a number of candidate objects of the target are selected from the extracted ROIs by a fast circle detection method. Afterwards, in the third stage, a robust feature extractor based on a combination of the output feature vectors from convolutional neural network (CNN), as a high-level feature extractor, and histogram of oriented gradients (HOG), as a low-level feature extractor, are used for representing features of various targets. Finally, the support vector machine (SVM) is employed for classification. The experimental results confirm that the proposed approach has good prediction accuracy and is able to reduce the false alarm rates.     

A modular clutter rejection technique for FLIR imagery using region-based principal component analysis

A modular clutter-rejection technique that uses region-based principal component analysis (PCA) is proposed. A major problem in FLIR ATR is the poorly centered targets generated by the preprocessing stage. Our modular clutter-rejection system usesstatic as well as dynamic region of interest (ROI) extraction to overcome the problem of poorly centered targets. In static ROI extraction, the center of the representative ROI coincides with the center of the potential target image. In dynamic ROI extraction, a representative ROI is moved in several directions with respect to the center of the potential target image to extract a number of ROIs. Each module in the proposed system applies region-based PCA to generate the feature vectors, which are subsequently used to make a decision about the identity of the potential target. Region-based PCA uses topological features of the targets to reject false alarms. In this technique, a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose using regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target. We also present experimental results using real-life data to evaluate and compare the performance of the clutter-rejection systems with static and dynamic ROI extraction.     

Interactive Selective and Adaptive Clustering for Detection of Microcalcifications in Mammograms

Estevez, L., Kehtarnavaz, N., and Wendt, R. III, Interactive Selective and Adaptive Clustering for Detection of Microcalcifications in Mammograms,Digital Signal Processing6(1996), 224–232.This paper presents a clustering algorithm, called interactive selective and adaptive clustering (Isaac), to assist radiologists in looking for small clusters of microcalcifications in mammograms. Isaac is developed to identify suspicious microcalcification regions which are missed by other classification techniques due to false positive samples in the feature space. It comprises two parts: (i) selective clustering and (ii) interactive adaptation. The first part reduces the number of false positives by identifying the microcalcification subspace or domains in the feature space. The second part allows the radiologist to improve results by interactively identifying additional false positive or true negative samples. Clinical evaluations of mammograms indicate the potential of using this algorithm as an effective tool to bring microcalcification areas to the attention of the radiologist during a routine reading session of mammograms.     

Texture-based features for classification of mammograms using decision tree

Mammogram—breast X-ray—is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesions exist, only 15–30 % of masses referred for surgical biopsy are actually malignant. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256 × 256 pixels size. The second step is the feature extraction; we used a set of 19 GLCM and GLRLM features, and the 19 (nineteen) features extracted from gray-level run-length matrix and gray-level co-occurrence matrix could distinguish malignant masses from benign masses with an accuracy of 96.7 %. Further analysis was carried out by involving only 12 of the 19 features extracted, which consists of 5 features extracted from GLCM matrix and 7 features extracted from GLRL matrix. The 12 selected features are as follows: Energy, Inertia, Entropy, Maxprob, Inverse, SRE, LRE, GLN, RLN, LGRE, HGRE, and SRLGE; ARM with 12 features as prediction can distinguish malignant mass image and benign mass with a level of accuracy of 93.6 %. Further analysis showed that area under the receiver operating curve was 0.995, which means that the accuracy level of classification is good or very good. Based on that data, it was concluded that texture analysis based on GLCM and GLRLM could distinguish malignant image and benign image with considerably good result. The third step is the classification process; we used the technique of decision tree using image content to classify between normal and cancerous masses. The proposed system was shown to have the large potential for cancer detection from digital mammograms.     

乳腺X线图像结构扭曲检测的研究

针对乳腺X线图像结构扭曲 （Architectural distortion,AD）检测假阳性率偏高的问题,提出了一种新的乳腺X线图像结构扭曲 检测方法相似度收敛指数（Similarity convergence index,SCI）方法.首先利用马氏距离比计算出毛刺的相似度,然后通过计算相似度加权的收敛指数增强放射状毛 刺,最后提取出收敛指数的局部最大值作为候选点,并对这些候选点进行分类,检测出结构扭曲. 该方法在Mini-MIAS （Mammographic Image Analysis Society）乳腺图像和北京大学人民医院乳腺中心乳腺图像上进行验证,实验结果表明,本文提出的方法有效降低了假阳 性率,同时适用于脂肪型乳腺X线图像和致密型乳腺X线图像.     

改进标记分水岭的乳腺X线图像肿块检测方法

乳腺X线图像肿块大小不一,固定参数的传统标记分水岭算法无法实现乳腺X线图像肿块的有效检测。针对此问题,文中提出了一种结合形状特征和改进型标记分水岭的乳腺X线图像肿块检测方法。在计算前景标记时,结合标记的形状特征判定前景标记,通过对前景标记进行形态学膨胀并提取边缘以获得背景标记,利用改进型自适应参数标记分水岭算法实现肿块检测。实验结果表明,文中算法通过结合形状特征,自适应地选择合适的形态学参数,使得肿块检测准确率高于传统标记分水岭算法。     

Automatic mass segmentation on mammograms combining random walks and active contour

Accurate mass segmentation on mammograms is a critical step in computer-aided diagnosis (CAD) systems. It is also a challenging task since some of the mass lesions are embedded in normal tissues and possess poor contrast or ambiguous margins. Besides, the shapes and densities of masses in mammograms are various. In this paper, a hybrid method combining a random walks algorithm and Chan-Vese (CV) active contour is proposed for automatic mass segmentation on mammograms. The data set used in this study consists of 1095 mass regions of interest (ROIs). First, the original ROI is preprocessed to suppress noise and surrounding tissues. Based on the preprocessed ROI, a set of seed points is generated for initial random walks segmentation. Afterward, an initial contour of mass and two probability matrices are produced by the initial random walks segmentation. These two probability matrices are used to modify the energy function of the CV model for prevention of contour leaking. Lastly, the final segmentation result is derived by the modified CV model, during which the probability matrices are updated by inserting several rounds of random walks. The proposed method is tested and compared with other four methods. The segmentation results are evaluated based on four evaluation metrics. Experimental results indicate that the proposed method produces more accurate mass segmentation results than the other four methods.     

基于模糊聚类方法的神经元形态分类识别

神经元形态分类识别是"人类脑计划"研究首要解决的问题。神经元真实形态复杂多样,利用物理观察和日常经验无法进行分类识别,传统的分类识别算法难以解决形态相似的神经元分类识别的误判现象。针对神经元形态分类误判与类别重叠问题,提出神经元几何形态特征提取方法,设计神经元形态特征自由分类模型,从而为神经元的精确分类、有效识别与新型命名提供方法支持和实践参考。实验结果表明,该分类模型具有较高的运行效率和聚类精度,较好地解决了分类误判和类别重叠问题。     

结合非监督分类和几何—纹理—光谱特征的高分影像道路提取

目的 目前针对复杂场景高分辨率遥感影像道路提取多采用监督分类方法,但需要人工选择样本,自动化程度低且具有不稳定性。基于像元级的方法,提取完整度低且易产生椒盐噪声;面向对象的方法易产生粘连问题。为了提高道路提取的完整度、准确度和自动化程度,提出一种基于非监督分类和几何—纹理—光谱特征的道路提取方法。方法 首先考虑光谱特征利用非监督分类进行初步分割,结合基于纹理特征分类的结果得到初始道路区域。然后根据道路特征建立一套完整的非道路区域滤除体系：边缘滤波断开道路和非道路的连接、纹理滤波滤除大面积非道路区域、形状滤波去除剩余小面积非道路区域。最后利用张量投票算法得到连贯、平滑的道路中心线。结果 选择复杂场景下的高分辨率IKONOS影像和QuickBird影像进行实验,与国内外基于像素和面向对象的两种有代表性的道路提取方法进行对比,采用完整率、正确率、检测质量3个评价指标进行定量评价。实验结果表明该方法相比于其他算法在完整率、正确率和检测质量上平均提高26.61%、5.57%和26.77%。定性分析结果表明,本文方法可以有效改善椒盐噪声和粘连现象。此外本文方法的自动化程度更高。结论 提出了一种基于非监督分类和几何—纹理—光谱特征的高分辨遥感影像道路提取方法,非监督相对于监督分类的方法有更高的自动化程度,复杂场景下的道路提取融合几何—纹理—光谱特征有效避免了基于像元级道路提取易产生的椒盐噪声现象和面向对象道路提取易产生的粘连现象。该方法适用于高分辨率遥感影像城市道路提取,能够得到较高的完整度、准确度以及自动化程度。非监督分类和多特征结合的道路提取方法有广阔的应用前景。     

Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images

Early detection of malignant melanoma skin cancer is crucial for treating the disease and saving lives. Many computerized techniques have been reported in the literature to diagnose and classify the disease with satisfactory skin cancer detection performance. However, reducing the false detection rate is still challenging and preoccupying because false positives trigger the alarm and require intervention by an expert pathologist for further examination and screening. In this paper, an automatic skin cancer diagnosis system that combines different textural and color features is proposed. New textural and color features are used in a bag-of-features approach for efficient and accurate detection. We particularly claim that the Histogram of Gradients (HG) and the Histogram of Lines (HL) are more suitable for the analysis and classification of dermoscopic and standard skin images than the conventional Histogram of Oriented Gradient (HOG) and the Histogram of Oriented Lines (HOL), respectively. The HG and HL are bagged separately using a codebook for each and then combined with other bagged color vector angles and Zernike moments to exploit the color information. The overall system has been assessed through intensive experiments using different classifiers on a dermoscopic image dataset and another standard dataset. Experimental results have shown the superiority of the proposed system over state-of-the-art techniques.     

Optimized Gabor features for mass classification in mammography

Gabor filter bank has been successfully used for false positive reduction problem and the discrimination of benign and malignant masses in breast cancer detection. However, a generic Gabor filter bank is not adapted to multi-orientation and multi-scale texture micro-patterns present in the regions of interest (ROIs) of mammograms. There are two main optimization concerns: how many filters should be in a Gabor filter band and what should be their parameters. Addressing these issues, this work focuses on finding optimizing Gabor filter banks based on an incremental clustering algorithm and Particle Swarm Optimization (PSO). We employ an SVM with Gaussian kernel as a fitness function for PSO. The effect of optimized Gabor filter bank was evaluated on 1024 ROIs extracted from a Digital Database for Screening Mammography (DDSM) using four performance measures (i.e., accuracy, area under ROC curve, sensitivity and specificity) for the above mentioned mass classification problems. The results show that the proposed method enhances the performance and reduces the computational cost. Moreover, the Wilcoxon signed rank test over the significance level of 0.05 reveals that the performance difference between the optimized Gabor filter bank and non-optimized Gabor filter bank is statistically significant.