Classification Of CT Images Research Articles

Classification of Lung Nodules using Improved Residual Convolutional Neural Network

The most common cancer of the lung cannot be ignored and can cause late-health death. Now CT can be used to help clinicians diagnose early-stage lung cancer. In certain cases the diagnosis of lung cancer detection is based on doctors' intuition, which can neglect other patients and cause complications. Deep learning in most other areas of medical diagnosis has proven to be a common and powerful tool. This research is planned for improving the residual evolutionary neural network (IRCNN). These networks apply with some changes to the benign and malignant lung nodule to the CT image classification task. The segmenting of the nodule is performed here by clustering k-means. The LIDC-IDRI database analysed those networks. Experimental findings show that the IRCNN network archived the best performance of lung nodule classification, which findings best among established methods.

Deep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of Lung Ct Images

Lung cancer is a serious illness which leads to increased mortality rate globally. The identification of lung cancer at the beginning stage is the probable method of improving the survival rate of the patients. Generally, Computed Tomography (CT) scan is applied for finding the location of the tumor and determines the stage of cancer. Existing works has presented an effective diagnosis classification model for CT lung images. This paper designs an effective diagnosis and classification model for CT lung images. The presented model involves different stages namely pre-processing, segmentation, feature extraction and classification. The initial stage includes an adaptive histogram based equalization (AHE) model for image enhancement and bilateral filtering (BF) model for noise removal. The pre-processed images are fed into the second stage of watershed segmentation model for effectively segment the images. Then, a deep learning based Xception model is applied for prominent feature extraction and the classification takes place by the use of logistic regression (LR) classifier. A comprehensive simulation is carried out to ensure the effective classification of the lung CT images using a benchmark dataset. The outcome implied the outstanding performance of the presented model on the applied test images.

Mil based lung CT-image classification using CNN

Tuberculosis (TB) is diagnosed using clinical settings through which the specimen taken as of the patient is examined. The availability of Mycobacterium tuberculosis bacteria (MTB) in that specimen confirms the existence of TB. The other examinations strongly recommend that, TB in the diagnosis, one cannot confirm it. A simple skin test is the most generally utilized diagnostic equipment for TB, though blood tests are becoming more common. A small quantity of a substance termed PPD (Post-Partum Depression) tuberculin is injected below the skin of one’s inside forearm. Sometimes, false negative treatment may also results. Thus several upcoming techniques are introduced to cure the TB. It is based upon the CAD system that deals with the problem of TB detection on CXR. It is maintained by the training dataset. The drawback that occurs on the previous model is that its inherent features are uncertain. The novel 3 methods implemented in this research are i) Lung Segment, ii) Texture Feature Extraction as well as iii) Pixel classification. Along with these, miSVM+PEDD (multiple instances Support Vector Machine + Probability Estimation and Data Discarding) is used for the segmentation process. Thus the model should be evaluated by implementing some recent features of the Multiple Instance Learning (MIL). The Improved Algorithm is deployed by training a MIL classifier which builds from other machine learning (ML), Active Learning (AL) and 1-class classification approaches. Here, the uncertainty intrinsic to a MIL pixel classifier is diminished while minimizing the labeling exertions. The uses of AL are centered on the image and text categorization. The resulting lung likelihood map is post-processed to obtain binary segmentation. Here, the MIL is integrated with the AL Model. The results are analyzed by contrasting the proposed system with the other prevailing techniques to prove the dominance of the proposed one.

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images.

Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.

Benign and malignant classification of renal occupancy lesions CT images using convolutional neural networks

Objective To investigate the feasibility and application value of the benign and malignant classificational methods of renal occupying CT images based on convolutional neural networks (CNN). Methods An image omics method that can automatically learn the image features and classify CT images was used. Firstly, the CNN model obtained by large-scale natural image training was used to migrate the characteristics of the renal occupancy lesions CT images, and then the fine-tuning of the full connection layer was used to realize the benign and malignant classification of the images. Results The evaluation indexes of the VGG19 model were lower than ResNet50 and Inception V3, and the training result showed obvious overfitting. The accuracy, sensitivity and negative prediction values of the Inception V3 model was 93.8%, 99.5% and 99.1%, respectively, which were higher than that of the ResNet50 model. Conclusions The benign and malignant classification of renal occupancy lesions CT images using CNN is a reasonable and feasible method, and the fine-tuned Inception V3 model has a better classification performance. Key words: Convolutional neural networks; Transfer learning; Renal occupancy lesions; CT images; Benign and malignant classification

An effective image denoising using PPCA and classification of CT images using artificial neural networks

The main aim of denoising is to remove the noise while recollecting as much possible important signal features. This appears to be very simple when considered under practical situations, where the type of images and noises are all variable parameters. This paper deals with removal of combination of noises from image and classification of normal and abnormal images. At first phase, median filter is used to remove the noises present in the images. To improve the denoised output, we are using PSM and PPCA with morphological operations, filter and region props. In the second phase, to analyse the denoised output, neural network-based classification is proposed. The use of artificial intelligent techniques for classification shows a great potential in this field. Hence the performance of neural network classifier is estimated in terms of training performance and classification accuracy and is compared with the existing method to show the system is effective.

An effective image denoising using PPCA and classification of CT images using artificial neural networks

An effective image denoising using PPCA and classification of CT images using artificial neural networks

On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images

Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.

Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images

A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a texture analysis methods to find and select the texture features of the tumor region of each slice to be segmented by support vector machine (SVM). The images considered for this study belongs to 208 benign and malignant tumor slices. The features are extracted and selected using Student's t-test. The reduced optimal features are used to model and train the probabilistic neural network (PNN) classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of quantitative measure of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features have important contribution in segmenting and classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed hybrid texture feature analysis method using Probabilistic Neural Network (PNN) based classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by Jaccard index, sensitivity, and specificity.

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features.

Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

Segmentation and Classification of Brain CT Images Using Combined Wavelet Statistical Texture Features

A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a method to find and select both the dominant run length and co-occurrence texture features of the wavelet approximation tumor region of each slice to be segmented by support vector machine. Two dimensional discrete wavelet decomposition is performed on the tumor image to remove the noise. The images considered for this study belong to 192 benign and malignant tumor slices. A total of 17 features are extracted and six features are selected using Student’s t test. The reduced optimal features are used to model and train the probabilistic neural network classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features that have important contribution in classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed system is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.

Automated Segmentation and Object Classification of CT Images: Application toIn VivoMolecular Imaging of Avian Embryos

Background. Although chick embryogenesis has been studied extensively, there has been growing interest in the investigation of skeletogenesis. In addition to improved poultry health and minimized economic loss, a greater understanding of skeletal abnormalities can also have implications for human medicine. True in vivo studies require noninvasive imaging techniques such as high-resolution microCT. However, the manual analysis of acquired images is both time consuming and subjective. Methods. We have developed a system for automated image segmentation that entails object-based image analysis followed by the classification of the extracted image objects. For image segmentation, a rule set was developed using Definiens image analysis software. The classification engine was implemented using the WEKA machine learning tool. Results. Our system reduces analysis time and observer bias while maintaining high accuracy. Applying the system to the quantification of long bone growth has allowed us to present the first true in ovo data for bone length growth recorded in the same chick embryos. Conclusions. The procedures developed represent an innovative approach for the automated segmentation, classification, quantification, and visualization of microCT images. MicroCT offers the possibility of performing longitudinal studies and thereby provides unique insights into the morpho- and embryogenesis of live chick embryos.

A new approach for classification of human brain CT images based on morphological operations

Automatic diagnosis may help to decrease human based diagnosis error and assist physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis. In this paper computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features, i.e. gray scale, symmetry and texture. Some classifiers were applied to classify normal and abnormal brain CT images. It showed that morphological operations can improve the result of accuracy. Moreover SVM classifier showed better result than other classifiers.

The linear attenuation coefficients as features of multiple energy CT image classification

We present in this paper an analysis of the linear attenuation coefficients as useful features of single and multiple energy CT images with the use of statistical pattern classification tools. We analyzed four CT images through two pointwise classifiers (the first classifier is based on the maximum-likelihood criterion and the second classifier is based on the k-means clustering algorithm) and one contextual Bayesian classifier (ICM algorithm – Iterated Conditional Modes) using an a priori Potts–Strauss model. A feature extraction procedure using the Jeffries–Matusita (J–M) distance and the Karhunen–Loève transformation was also performed. Both the classification and the feature selection procedures were found to be in agreement with the predicted discrimination given by the separation of the linear attenuation coefficient curves for different materials.

Segmentation and Classification of Jaw Bone CT images using Curvelet based Texture features

The evaluation of jaw bone trabecular structure and quality could be useful for characterization and response of the bone for dental implants. Current clinical methods for assessment of bone quality at the implant sites largely depend on assessing bone mineral density using Dual energy X-ray absorptionometry. However, this does not provide any information about bone structure which is considered to be an equally important factor in assessing bone quality. This paper presents a novel approach for computer analysis of trabecular (or cancellous) bone structure using multiresolution based texture analysis to evaluate changes taking place in the architecture of bone with age and gender. The findings are compared with Hounsfield Units measured from the CT machine at different sites, which is a standard reference. Fifty patients were subjected to clinical CT to obtain the CT number and texture based architectural parameters respectively. In each site texture features were extracted using gray level co-occurrence matrices (GLCM), Run length matrices, Histogram and curvelet based statistical & co occurrence analysis. A very difficult problem in classification techniques is the choice of features to distinguish between classes. However the performance of any classifier is not optimized when all features are used. The feature optimization problem is addressed using Principle component analysis in terms of the best recognition rate and the optimal number of features. Testing this on a series of 120 image sections of trabecular bone with normal, partial and total edentulous patients correctly classified over 90% of the porous bone group with an overall accuracy of 87.8%-95.2%.The results shows that by using the Classification & Regression Tree approach the combination of the features from gray level and Ist order statistics achieved overall classification accuracy in the range of 87.8- 90.24%. Features selected from the curvelet based co occurrence matrix performed better with overall classification accuracy of 92.89%.In order to increase the success rate the classification is done using the combination of curvelet statistical features and curvelet co occurrence features as feature vector and using this, a mean success rate of 95.2% is obtained. Keywords: Multiresolution analysis; Texture features; curvelets; Computed Tomography; Regression analysis; GLRLM. DOI: 10.3329/bjms.v9i1.5229 Bangladesh Journal of Medical Science Vol.09 No.1 Jan 2010 33-43

Chronic acceleration effects on bone development in the chick embryo.

ARTICLESChronic acceleration effects on bone development in the chick embryoDR ReddenDR ReddenPublished Online:01 Jan 1970https://doi.org/10.1152/ajplegacy.1970.218.1.310MoreSectionsPDF (789 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat Previous Back to Top Next Download PDF FiguresReferencesRelatedInformation Cited ByAutomated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian EmbryosInternational Journal of Biomedical Imaging, Vol. 2013Organ Culture of BoneBone and CartilageInfluence of Gravity on the Maintenance Feed Requirement of ChickensThe Journal of Nutrition, Vol. 101, No. 1 More from this issue > Volume 218Issue 1January 1970Pages 310-313 Copyright & PermissionsCopyright © 1970 by American Physiological Societyhttps://doi.org/10.1152/ajplegacy.1970.218.1.310PubMed5409889History Published online 1 January 1970 Published in print 1 January 1970 Metrics