scholarly journals Automatic Pulmonary Nodule Growth Measurement through CT Image Analysis based on Morphology Filtering and Statistical Region Merging

2018 ◽  
Vol 11 (3) ◽  
pp. 1247-1259
Author(s):  
Elaheh Aghabalaei Khordehchi ◽  
Ahmad Ayatollahi ◽  
Mohammad Reza Daliri
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Treatment Session ◽  
Support Vector ◽  
Svm Classifier ◽  
Ct Image ◽  
Region Merging ◽  
Growth Measurement ◽  
Statistical Region Merging

This paper proposes an innovative method for automatic detection of pulmonary nodules in Computed Tomography (CT) data and measurement of changes in the number and sizes of the detected nodules during the treatment session. In the presented method, two multi-slice CT images are first taken from the patient’s lung, each captured by a similar capturing device but at two different dates. The CT images are then analyzed and their pulmonary nodules are extracted using a novel framework based on Mathematical Morphology Filtering (MMF), Statistical Region Merging (SRM), and Support Vector Machines (SVM). The MMF step smoothes the image in order to increase its homogeneity as well as removing the noises and artifacts. The SRM algorithm segments each slice of the CT image. After connecting the boundaries of the segments in adjacent slices, three-dimensional objects are produced which are considered as nodule-candidates. These candidates are classified into nodules and non-nodules using a two-class SVM classifier. The extracted nodules in each image are then labeled and their characteristics (i.e. labels, locations, and sizes) are stored. Finally, after registering the image pair using an affine algorithm, the growth rates of the lung nodules are measured.

scholarly journals AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

2017 ◽  
Vol 36 (2) ◽  
pp. 65 ◽  
Author(s):  
Elaheh Aghabalaei Khordehchi ◽  
Ahmad Ayatollahi ◽  
Mohammad Reza Daliri
Keyword(s):  
Lung Nodule ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Region Merging ◽  
Three Dimensional Objects ◽  
Ct Data ◽  
Statistical Region Merging

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

scholarly journals A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

2014 ◽  
Vol 33 (1) ◽  
pp. 13 ◽  
Author(s):  
Mehdi Alilou ◽  
Vassili Kovalev ◽  
Eduard Snezhko ◽  
Vahid Taimouri
Keyword(s):  
Early Stage ◽  
Pulmonary Nodules ◽  
Maximum Intensity Projection ◽  
Region Growing ◽  
Ct Images ◽  
Support Vector ◽  
Solitary Pulmonary Nodules ◽  
Computed Tomography Images ◽  
Lung Structure ◽  
Multiple Thresholds

Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx) framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM) is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC) image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9). Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP) volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.

scholarly journals Research on automatic identification technique of CT image in lung

2018 ◽  
Vol 15 (3) ◽  
pp. 501-515
Author(s):  
Zhijie Zhao ◽  
Cong Ren ◽  
Huadong Sun ◽  
Zhipeng Fan ◽  
Ze Gao
Keyword(s):  
Human Cancer ◽  
Pulmonary Nodules ◽  
Lung Parenchyma ◽  
Support Vector ◽  
Automatic Identification ◽  
Ct Image ◽  
Cancer Disease ◽  
Identification Technique ◽  
Pulmonary Parenchyma ◽  
Occurrence Matrix

Lung cancer has become the world's human cancer disease in the "first killer." In this paper, three aspects of lung CT images were treated. Firstly, based on the CT image preprocessing, the lung parenchyma was segmented by random walk algorithm and the ROI was extracted from the pulmonary parenchyma; Secondly, the 10-dimensional feature vectors of pulmonary nodule ROI were extracted by the gray level co-occurrence matrix algorithm; Finally, support vector machine as a classifier is to identify the pulmonary nodules and the accuracy rate is more than 94%. The experimental results show that the study of automatic CT image recognition can provide some data reference for doctors and play a supporting role in the course of treatment.

scholarly journals Three-Dimensional Detection of Pulmonary Nodules in Chest CT Images

2011 ◽  
Vol 4 (5) ◽  
Author(s):  
Aliaa A. A. Youssif ◽  
Shereen A. Hussein ◽  
Ahmed S. Ibrahim
Keyword(s):  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Chest Ct

scholarly journals Supervised Image Classification Algorithm Using Representative Spatial Texture Features: Application to COVID-19 Diagnosis Using CT Images

2020 ◽  
Author(s):  
Zehor Belkhatir ◽  
Raúl San José Estépar ◽  
Allen R. Tannenbaum
Keyword(s):  
Image Classification ◽  
Virus Disease ◽  
Texture Features ◽  
Ct Images ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Svm Classifier ◽  
Wasserstein Metric ◽  
Supervised Image Classification ◽  
Representative Samples

AbstractAlthough there is no universal definition for texture, the concept in various forms is nevertheless widely used and a key element of visual perception to analyze images in different fields. The present work’s main idea relies on the assumption that there exist representative samples, which we refer to as references as well, i.e., “good or bad” samples that represent a given dataset investigated in a particular data analysis problem. These representative samples need to be accounted for when designing predictive models with the aim of improving their performance. In particular, based on a selected subset of texture gray-level co-occurrence matrices (GLCMs) from the training cohort, we propose new representative spatial texture features, which we incorporate into a supervised image classification pipeline. The pipeline relies on the support vector machine (SVM) algorithm along with Bayesian optimization and the Wasserstein metric from optimal mass transport (OMT) theory. The selection of the best, “good and bad,” GLCM references is considered for each classification label and performed during the training phase of the SVM classifier using a Bayesian optimizer. We assume that sample fitness is defined based on closeness (in the sense of the Wasserstein metric) and high correlation (Spearman’s rank sense) with other samples in the same class. Moreover, the newly defined spatial texture features consist of the Wasserstein distance between the optimally selected references and the remaining samples. We assessed the performance of the proposed classification pipeline in diagnosing the corona virus disease 2019 (COVID-19) from computed tomographic (CT) images.

Classifying Lithofacies from Textural Features in Whole Core CT-Scan Images

2021 ◽  
pp. 1-17
Author(s):  
Kurdistan Chawshin ◽  
Carl F. Berg ◽  
Damiano Varagnolo ◽  
Andres Gonzalez ◽  
Zoya Heidari ◽  
...  
Keyword(s):  
Principal Component ◽  
Ct Images ◽  
Nondestructive Method ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Statistical Features ◽  
Data Set ◽  
Unseen Data ◽  
Core Description

Summary X-ray computerized tomography (CT) is a nondestructive method of providing information about the internal composition and structure of whole core reservoir samples. In this study we propose a method to classify lithology. The novelty of this method is that it uses statistical and textural information extracted from whole core CT images in a supervised learning environment. In the proposed approaches, first-order statistical features and textural grey-levelco-occurrence matrix (GLCM) features are extracted from whole core CT images. Here, two workflows are considered. In the first workflow, the extracted features are used to train a support vector machine (SVM) to classify lithofacies. In the second workflow, a principal component analysis (PCA) step is added before training with two purposes: first, to eliminate collinearity among the features and second, to investigate the amount of information needed to differentiate the analyzed images. Before extracting the statistical features, the images are preprocessed and decomposed using Haar mother wavelet decomposition schemes to enhance the texture and to acquire a set of detail images that are then used to compute the statistical features. The training data set includes lithological information obtained from core description. The approach is validated using the trained SVM and hybrid (PCA + SVM) classifiers to predict lithofacies in a set of unseen data. The obtained results show that the SVM classifier can predict some of the lithofacies with high accuracy (up to 91% recall), but it misclassifies, to some extent, similar lithofacies with similar grain size, texture, and transport properties. The SVM classifier captures the heterogeneity in the whole core CT images more accurately compared with the core description, indicating that the CT images provide additional high-resolution information not observed by manual core description. Further, the obtained prediction results add information on the similarity of the lithofacies classes. The prediction results using the hybrid classifier are worse than the SVM classifier, indicating that low-power components may contain information that is required to differentiate among various lithofacies.

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