Automated lung nodule detection and classification based on multiple classifiers voting

Lung cancer is the most common cause of cancer‐related death globally. Currently, lung nodule detection and classification are performed by radiologist‐assisted computer‐aided diagnosis systems. However, emerged artificially intelligent techniques such as neural network, support vector machine, and HMM have improved the detection and classification process of cancer in any part of the human body. Such automated methods and their possible combinations could be used to assist radiologists at early detection of lung nodules that could reduce treatment cost, death rate. Literature reveals that classification based on voting of classifiers exhibited better performance in the detection and classification process. Accordingly, this article presents an automated approach for lung nodule detection and classification that consists of multiple steps including lesion enhancement, segmentation, and features extraction from each candidate's lesion. Moreover, multiple classifiers logistic regression, multilayer perceptron, and voted perceptron are tested for the lung nodule classification using k‐fold cross‐validation process. The proposed approach is evaluated on the publically available Lung Image Database Consortium benchmark data set. Based on the performance evaluation, it is observed that the proposed method performed better in the stateof the art and achieved an overall accuracy rate of 100%.     

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker‐based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker‐based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi‐square max conditional priority features approach. In the later step, selected features are fused using a serial‐based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.     

Quantitative microscopy approach for shape‐based erythrocytes characterization in anaemia

Anaemia is one of the most common diseases in the world population. Primarily anaemia is identified based on haemoglobin level; and then microscopically examination of peripheral blood smear is required for characterizing and confirmation of anaemic stages. In conventional approach, experts visually characterize abnormality present in the erythrocytes under light microscope, and this evaluation process is subjective in nature and error prone. In this study, we have proposed a methodology using machine learning techniques for characterizing erythrocytes in anaemia associated with anaemia using microscopic images of peripheral blood smears. First, peripheral blood smear images are preprocessed based on grey world assumption technique and geometric mean filter for reducing unevenness of background illumination and noise reduction. Then erythrocyte cells are segmented using marker‐controlled watershed segmentation technique. The erythrocytes in anaemia, such as, tear drop, echinocyte, acanthocyte, elliptocyte, sickle cells and normal erythrocytes cells have been characterized and classified based on their morphological changes. Optimal subset of features, ranked by information gain measure provides highest classification performance using logistic regression classifier in comparison with other standard classifiers.     

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears

Visual inspection for the quantification of malaria parasitaemiain (MP) and classification of life cycle stage are hard and time taking. Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k‐nearest neighbor, Naïve Bayes and multi‐class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non‐infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing .     

A new texture and shape based technique for improving meningioma classification

Over the past decade, computer‐aided diagnosis is rapidly growing due to the availability of patient data, sophisticated image acquisition tools and advancement in image processing and machine learning algorithms. Meningiomas are the tumors of brain and spinal cord. They account for 20% of all the brain tumors. Meningioma subtype classification involves the classification of benign meningioma into four major subtypes: meningothelial, fibroblastic, transitional, and psammomatous. Under the microscope, the histology images of these four subtypes show a variety of textural and structural characteristics. High intraclass and low interclass variabilities in meningioma subtypes make it an extremely complex classification problem. A number of techniques have been proposed for meningioma subtype classification with varying performances on different subtypes. Most of these techniques employed wavelet packet transforms for textural features extraction and analysis of meningioma histology images. In this article, a hybrid classification technique based on texture and shape characteristics is proposed for the classification of meningioma subtypes. Meningothelial and fibroblastic subtypes are classified on the basis of nuclei shapes while grey‐level co‐occurrence matrix textural features are used to train a multilayer perceptron for the classification of transitional and psammomatous subtypes. On the whole, average classification accuracy of 92.50% is achieved through the proposed hybrid classifier; which to the best of our knowledge is the highest. Microsc. Res. Tech. 77:862–873, 2014. © 2014 Wiley Periodicals, Inc.     

Automated discrimination of dicentric and monocentric chromosomes by machine learning‐based image processing

Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa‐stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2‐4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ = 1.3, TPR = 0.52 and PPV = 0.83, while at σ = 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and σ = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures. Microsc. Res. Tech. 79:393–402, 2016. © 2016 Wiley Periodicals, Inc.     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.     

A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis

Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg–Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.     

Comparisons of the topographic characteristics and electrical charge distributions among Babesia‐infected erythrocytes and extraerythrocytic merozoites using AFM

Tick‐borne Babesia parasites are responsible for costly diseases worldwide. Improved control and prevention tools are urgently needed, but development of such tools is limited by numerous gaps in knowledge of the parasite–host relationships. We hereby used atomic force microscopy (AFM) and frequency‐modulated Kelvin probe potential microscopy (FM‐KPFM) techniques to compare size, texture, roughness and surface potential of normal and infected Babesia bovis, B. bigemina and B. caballi erythrocytes to better understand the physical properties of these parasites. In addition, AFM and FM‐KPFM allowed a detailed view of extraerythrocytic merozoites revealing shape, topography and surface potential of paired and single parasites. B. bovis‐infected erythrocytes display distinct surface texture and overall roughness compared to noninfected erythrocytes. Interestingly, B. caballi‐infected erythrocytes do not display the surface ridges typical in B. bovis parasites. Observations of extraerythrocytic B. bovis, B. bigemina and B. caballi merozoites using AFM revealed differences in size and shape between these three parasites. Finally, similar to what was previously observed for Plasmodium‐infected erythrocytes, FM‐KPFM images reveal an unequal electric charge distribution, with higher surface potential above the erythrocyte regions that are likely associated with Babesia parasites than over its remainder regions. In addition, the surface potential of paired extraerythrocytic B. bovis Mo7 merozoites revealed an asymmetric potential distribution. These observations may be important to better understand the unique cytoadhesive properties of B. bovis‐infected erythrocytes, and to speculate on the role of differences in the distribution of surface charges in the biology of the parasites.     

Accurate automatic detection of acute lymphatic leukemia using a refined simple classification

An improved classification technique is presented to identify automatically the acute lymphatic leukemia (ALL) subtypes. An adaptive segmentation procedure is performed on peripheral blood smear images to extract the main features (10 geometric features) from the segmented images of white blood cell (WBC), nucleus, and cytoplasm. To show the importance of the different extracted features for the diagnostic accuracy, a comprehensive study is made on all the possible permutation cases of the features using powerful classifiers which are K‐nearest neighbor (KNN) at different metric functions, support vector machine (SVM) with different kernels, and artificial neural network (ANN). This procedure enables us to construct a feature map depending only on least number of features which lead to the highest diagnostic accuracy. It is found that the features map regarding the vacuoles in the cytoplasm and the regularity of the nucleus membrane gives the highest accurate results. The automatic classification for ALL subtypes based only on these two effective features is assessed using the receiver operating characteristic (ROC) curve and F ₁ ‐score measures. It is confirmed that the present technique is highly accurate, and saves the effort and time of training.     

An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images

Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio‐marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre‐processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two‐step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images. Microsc. Res. Tech. 77:547–559, 2014. © 2014 Wiley Periodicals, Inc.     

Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique

Imprint cytology (IC) refers to one of the most reliable, rapid and affordable techniques for breast malignancy screening; where shape variation of H&E stained nucleus is examined by the pathologists. This work aims at developing an automated and efficient segmentation algorithm by integrating Lagrange's interpolation and superpixels in order to delineate overlapped nuclei of breast cells (normal and malignant). Subsequently, a computer assisted IC tool has been designed for breast cancer (BC) screening. The proposed methodology consists of mainly three subsections: gamma correction for preprocessing, single nuclei segmentation and segmentation of overlapping nuclei. Single nuclei segmentation combines histogram‐based thresholding and morphological operations; where segmentation of overlapping nuclei includes concave point detection, Lagrange's interpolation for overlapping arc area detection and the fine segmentation of overlapped arc area by superpixels. Total 16 significant features (p < 0.05) quantifying shape and texture of nucleus were extracted, and random forest (RF) classifier was skilled for automated screening. The proposed methodology has been tested on 120 IC images (approximately 12 000 nuclei); where 98% segmentation accuracy and 99% classification accuracy were achieved. Besides, performance evaluation was studied by using Jaccard's index (= 94%), correlation coefficient (= 95%), Dice similarity coefficient (= 97%) and Hausdorff distance (= 43%). The proposed approach could offer benefit to the pathologists for confirmatory BC screening with improved accuracy and could potentially lead to a better shape understanding of malignant nuclei.     

Intelligent microscopic approach for identification and recognition of citrus deformities

Plant diseases are accountable for economic losses in an agricultural country. The manual process of plant diseases diagnosis is a key challenge from last one decade; therefore, researchers in this area introduced automated systems. In this research work, automated system is proposed for citrus fruit diseases recognition using computer vision technique. The proposed method incorporates five fundamental steps such as preprocessing, disease segmentation, feature extraction and reduction, fusion, and classification. The noise is being removed followed by a contrast stretching procedure in the very first phase. Later, watershed method is applied to excerpt the infectious regions. The shape, texture, and color features are subsequently computed from these infection regions. In the fourth step, reduced features are fused using serial‐based approach followed by a final step of classification using multiclass support vector machine. For dimensionality reduction, principal component analysis is utilized, which is a statistical procedure that enforces an orthogonal transformation on a set of observations. Three different image data sets (Citrus Image Gallery, Plant Village, and self‐collected) are combined in this research to achieving a classification accuracy of 95.5%. From the stats, it is quite clear that our proposed method outperforms several existing methods with greater precision and accuracy.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

融合下补偿结构的眼底血管图像分割网络研究

眼底血管图像在临床中通常被用于眼部疾病的诊断及监测，其中血管的形态结构能够反映疾病的重要特征，因此，眼底血管图像的分割处理对眼部疾病的诊断和预防具有十分重要的医学意义。针对目前人工智能主流算法中卷积和池化操作会导致很多特征丢失，提取特征时会忽视图像中的空间信息，图像中的细小血管很难分割出来等问题，基于U-net模型进行了相关研究，结合空间注意力模块对空间特征进行细化，同时提出了一种下补偿结构LCSAnet。该结构能够减少网络提取特征信息过程中的特征损失，从而提高分割精度。研究实验在DRIVE数据集上完成，LC-SAnet的分割准确率达到96.97%,F1值达到74.36%。结果证明，LC-SAnet表现出更好的分割性能，对细小血管的结构识别更加准确。     

多阶段边界参考网络的动作分割

针对现有动作分割算法中过分割问题导致预测错误、造成分割质量下降的现象,提出一种可调视频动作边界信息作为参考的多阶段参考网络,在基于多阶段时间卷积网络的主干网络中,为每个阶段独立引入视频动作边界信息作为参考.各阶段使用相同的边界信息会使模型固化,为使主干网络能够调整参与各阶段输出计算的边界值,对不同样本区分处理,提出多层...     

Computer aided detection of ischemic stroke using segmentation and texture features

Computed tomography images are widely used in the diagnosis of ischemic stroke because of its faster acquisition and compatibility with most life support devices. This paper presents a new approach to automated detection of ischemic stroke using segmentation, midline shift and image feature characteristics, which separate the ischemic stroke region from healthy tissues in computed tomography images. The proposed method consists of five stages namely, pre-processing, segmentation, tracing midline of the brain, extraction of texture features and classification. The application of the proposed method for early detection of ischemic stroke is demonstrated to improve efficiency and accuracy of clinical practice. The results are quantitatively evaluated by a human expert. The average overlap metric, average precision and average recall between the results obtained using the proposed approach and the ground truth are 0.98, 0.99 and 0.98, respectively. A classification with accuracy of 98%, 97%, 96% and 92% has been obtained by SVM, k-NN, ANN and decision tree.     

Hybrid feature extraction techniques for microscopic hepatic fibrosis classification

Chronic liver diseases' hallmark is the fibrosis that results in liver function failure in advanced stages. One of the serious parasitic diseases affecting the liver tissues is schistosomiasis. Immunologic reactions to Schistosoma eggs leads to accumulation of collagen in the hepatic parenchyma causing fibrosis. Thus, monitoring and reporting the staging of the histopathological information related to liver fibrosis are essential for accurate diagnosis and therapy of the chronic liver diseases. Automated assessment of the microscopic liver tissue images is an essential process. For accurate and timeless assessment, an automated image analysis and classification of different stages of fibrosis can be employed as an efficient procedure. In this work, granuloma stages, namely cellular, fibrocellular, and fibrotic granulomas along with normal liver samples were classified after features extraction. In this work, a new hybrid combination of statistical features with empirical mode decomposition (EMD) is proposed. These combined features are further classified using the back‐propagation neural network (BPNN). A comparative study of the used classifier with the support vector machine is also conducted. The comparative results established that the BPNN achieved superior accuracy of 98.3% compared to the linear SVM, quadratic SVM, and cubic SVM that provided 85%, 84%, and 80%; respectively. In conclusion, this work is of special value that provides promising results for early prediction of the liver fibrosis in schistosomiais and other fibrotic liver diseases in no time with expected better prognosis after treatment.     

灰度直方图和支持向量机在磁环外观检测中的应用

本文提出了一套基于灰度直方图和支持向量机的磁环自动分类系统。为了用低维的灰度信息来描述磁环的特征，提出了一套图像处理的算法。将图像从背景分离之后，进行灰度直方图处理来提取灰度特征。接着采用主分量分析法，将灰度统计信息由256维向量降低到20维向量，以这20维向量作为输入，用支持向量机进行分类。最后，经过训练得到最优分类函数，分类正确率达到97．3％。     

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK‐1, ISIC MSK‐2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.