scholarly journals CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature Analysis

2019 ◽  
Author(s):  
Jason Causey ◽  
Jake Qualls ◽  
Jason H. Moore ◽  
Fred Prior ◽  
Xiuzhen Huang
Keyword(s):  
Image Analysis ◽  
Quantitative Imaging ◽  
Computer Systems ◽  
Lung Cancer Screening ◽  
Image Features ◽  
Image Feature ◽  
Detection Accuracy ◽  
Ct Image ◽  
Imaging Tool ◽  
Lung Ct

AbstractBackgroundLung CT scans are widely used for lung cancer screening and diagnosis. Current research focuses on quantitative analytics (radiomics) to improve screening and detection accuracy. However there are very limited numbers of portable software tools for automatic lung CT image analysis.ResultsHere we build a Docker container, CNNcon, as a quantitative imaging tool for analyzing lung CT image features. CNNcon is developed from our recently published algorithm for nodule analysis, based on convolutional neural networks (CNN). When provided with a list of the centroid coordinates of regions of interest (ROI) in a volumetric CT study containing potential lung nodules, CNNcon can automatically generate highly accurate malignancy prediction of each ROI. CNNcon can also generate a vector of image features of each ROI, to facilitate further analyses by combining image features and other clinical features. As a Docker container, CNNcon is portable to various computer systems, convenient to install, and easy to use. CNNcon was tested on different computer systems and generated identical results.ConclusionsWe anticipate that CNNcon will be a useful tool and broadly acceptable to the research community interested in quantitative image analysis.AvailabilityCNNcon and document are publicly available and can be downloaded from the website: http://bioinformatics.astate.edu/CNN-Container/

scholarly journals Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

Tumor Biology ◽  
2017 ◽  
Vol 39 (3) ◽  
pp. 101042831769455 ◽  
Author(s):  
Jia-Mei Chen ◽  
Yan Li ◽  
Jun Xu ◽  
Lei Gong ◽  
Lin-Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Image Features ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Image Feature ◽  
Prognostic Models ◽  
Future Research ◽  
Hematoxylin And Eosin ◽  
Histopathology Images

With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

scholarly journals DATA PROCESSING APPROACHES FOR LUNG CT- IMAGE ANALYSIS UNDER RESOURCE CONSTRAINTS

2019 ◽  
Author(s):  
Aleksandra Vatian ◽  
Artyom Lobantsev ◽  
Nikita Gorokhov ◽  
Mikhail Mirzayanov ◽  
Georgii Korneev ◽  
...  
Keyword(s):  
Image Analysis ◽  
Data Processing ◽  
Resource Constraints ◽  
Ct Image ◽  
Ct Image Analysis ◽  
Lung Ct

scholarly journals Combining CT Images and Clinical Features of Four Periods to Predict Whether Patients Have Rectal Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yingyin Feng ◽  
Qi Ding ◽  
Chen Meng ◽  
Wenfeng Wang ◽  
Jingjing Zhang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Random Forest ◽  
Area Under The Curve ◽  
Image Features ◽  
Learning System ◽  
Image Feature ◽  
Good Prediction ◽  
Ct Image ◽  
Clinical Indicators ◽  
Prediction Result

In this paper, we mainly use random forest and broad learning system (BLS) to predict rectal cancer. A total of 246 participants with computed tomography (CT) image records were enrolled. The total model in the training set (combined with imaging and clinical indicators) has the best prediction result, with the area under the curve (AUC) of 0.999 (95% confidence internal (CI): 0.996–1.000) and the accuracy of 0.990 (95%CI: 0.976–1.000). Model 3, the general model in the test set, has the best prediction result, with the AUC of 0.962 (95%CI: 0.915–1.000) and the accuracy of 0.920 (95%CI: 0.845–0.995). The results of the model using random forest prediction are compared with those using BLS prediction. It can be found that there is no statistical difference between the two results. Our prediction model combined with image features has a good prediction result, and this image feature is the most important among all features. Consequently, we can successfully predict rectal cancer through a combination of the clinical indicators and the comprehensive indicators of CT image characteristics in four different periods (plain scan, vein, artery, and excretion).

Quantitative CT imaging features for hepatocellular carcinoma (HCC) with b-catenin (CTNNB1) gene mutation.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 253-253
Author(s):  
Ahmed M. Khalaf ◽  
David T. Fuentes ◽  
Kareem Ahmed ◽  
Reham Abdel-Wahab ◽  
Manal Hassan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Gene Mutation ◽  
Liver Tissue ◽  
Quantitative Imaging ◽  
Image Features ◽  
Ct Imaging ◽  
Image Feature ◽  
Imaging Feature ◽  
Imaging Features ◽  
Ct Studies

253 Background: To determine whether CT imaging features can provide quantitative biomarkers to differentiate HCC with pathologic B-catenin gene mutation and those without mutation. Methods: Quantitative imaging features were extracted from a database of manually labeled liver with enhancing and non-enhancing tumor tissue,which were established using multiphasic CT images from 17 patients. CT studies were done before each patient underwent surgical removal of the HCC, which were subjected to pathologic analysis to evaluate B-catenin mutation.The mean period between the CT studies and the pathologic analyses was 18 days. According to the pathology results, the patients were divided into two groups: HCC with CTNNB1 mutation and HCC without. Image feature extraction included image gradients, co-occurrence matrix, and pixel neighborhood statistics of the first, second, and third moments. Pairwise analyses of the imaging features were performed on the mutated and non-mutated HCC images and the background liver tissue of both groups. Independent samples t-test and Mann Whitney U test were performed to quantitatively compare between the means of the imaging features extracted from the tumor tissues of both groups and those extracted from the background liver tissue of both groups. Results: Imaging feature analysis of the pairwise difference between the mutated and non-mutated HCC scans for multiple pixel-neighborhood image features are statistically significant.The top stratifying image features include the skewness (p = 0.02), energy (p = .03), and entropy (p = .03) during the venous and arterial phase. Conclusions: This preliminary study demonstrates the feasibility of quantitative imaging feature extraction from CE-CT imaging to differentiate between HCC with proven B-catenin gene mutation and those without mutation. Non-invasive methods of identifying HCC with B-catenin mutations may be clinically beneficial since B-catenin is an important potential target in novel cancer therapies, and identifying B-catenin mutations may also help provide information regarding prognosis.Verifying the quantitative features in larger patient populations is needed to confirm the results of this study.

scholarly journals Feature Extraction for Medical CT Images of Sports Tear Injury

2020 ◽  
Author(s):  
Qi Nie ◽  
Ye-bing Zou ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Feature Extraction ◽  
Auxiliary Information ◽  
Low Frequency ◽  
Frequency Component ◽  
Ct Images ◽  
Image Features ◽  
Image Feature ◽  
Ct Image ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

Abstract Analysis of medical CT images directly affects the accuracy of clinical case diagnosis. Therefore, feature extraction problem of medical CT images is extremely important. A feature extraction algorithm for medical CT images of sports tear injury is proposed. First, CT images are decomposed into a low frequency component and a series of high frequency components in different directions by wavelet fast decomposition method. The high- and low-frequency information of CT images is enhanced by wavelet layered multi-directional image enhancement algorithm, and the multi-scale enhancement for medical CT images of sports tear injury is completed. Then, edge of the enhanced CT images is extracted using an image edge extraction algorithm based on extended mathematical morphology. Finally, based on the extracted edge information of CT images, feature extraction for medical CT images of sports tear injury is completed by the NSCT-GLCM based CT image feature extraction algorithm. Research results show that the proposed algorithm effectively extracts CT image features of sports tear injury and provides auxiliary information for doctor diagnosis.

scholarly journals Effect of Quantitative Nuclear Image Features on Recurrence of Ductal Carcinoma In Situ (DCIS) of the Breast

2008 ◽  
Vol 6 ◽  
pp. CIN.S401 ◽  
Author(s):  
David E. Axelrod ◽  
Naomi A. Miller ◽  
H. Lavina Lickley ◽  
Jin Qian ◽  
William A. Christens-Barry ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Image Features ◽  
Nuclear Grade ◽  
Image Feature ◽  
Group A ◽  
Group B ◽  
Nuclear Features

Background Nuclear grade has been associated with breast DCIS recurrence and progression to invasive carcinoma; however, our previous study of a cohort of patients with breast DCIS did not find such an association with outcome. Fifty percent of patients had heterogeneous DCIS with more than one nuclear grade. The aim of the current study was to investigate the effect of quantitative nuclear features assessed with digital image analysis on ipsilateral DCIS recurrence. Methods Hematoxylin and eosin stained slides for a cohort of 80 patients with primary breast DCIS were reviewed and two fields with representative grade (or grades) were identified by a Pathologist and simultaneously used for acquisition of digital images for each field. Van Nuys worst nuclear grade was assigned, as was predominant grade, and heterogeneous grading when present. Patients were grouped by heterogeneity of their nuclear grade: Group A: nuclear grade 1 only, nuclear grades 1 and 2, or nuclear grade 2 only (32 patients), Group B: nuclear grades 1, 2 and 3, or nuclear grades 2 and 3 (31 patients), Group 3: nuclear grade 3 only (17 patients). Nuclear fine structure was assessed by software which captured thirty-nine nuclear feature values describing nuclear morphometry, densitometry, and texture. Step-wise forward Cox regressions were performed with previous clinical and pathologic factors, and the new image analysis features. Results Duplicate measurements were similar for 89.7% to 97.4% of assessed image features. The rate of correct classification of nuclear grading with digital image analysis features was similar in the two fields, and pooled assessment across both fields. In the pooled assessment, a discriminant function with one nuclear morphometric and one texture feature was significantly (p = 0.001) associated with nuclear grading, and provided correct jackknifed classification of a patient's nuclear grade for Group A (78.1%), Group B (48.4%), and Group C (70.6%). The factors significantly associated with DCIS recurrence were those previously found, type of initial presentation (p = 0.03) and amount of parenchymal involvement (p = 0.05), along with the morphometry image feature of ellipticity (p = 0.04). Conclusion Analysis of nuclear features measured by image cytometry may contribute to the classification and prognosis of breast DCIS patients with more than one nuclear grade.

FRFCM clustering segmentation method for medical CT image feature diagnosis

2020 ◽  
pp. 1-9
Author(s):  
Qian He ◽  
Juwei Shao ◽  
Jian Pu ◽  
Minjie Zhou ◽  
M. M ◽  
...  
Keyword(s):  
Fuzzy Set ◽  
Medical Image ◽  
Intuitionistic Fuzzy Set ◽  
Image Features ◽  
Clinical Diagnostics ◽  
Image Feature ◽  
Ct Image ◽  
Clustering Methods ◽  
Segmentation Method ◽  
Clustering Method

Medical image recognition is affected by characteristics such as blur and noise, which cause medical image features that cannot be effectively identified and directly affects clinical diagnostics. In order to improve the diagnostic effect of medical CT image features, based on the FRFCM clustering segmentation method, this study combines the medical CT image feature reality, collects data for traditional clustering method analysis, and sorts out the shortcomings of traditional clustering methods. Simultaneously, this study improves the traditional clustering method by combining medical image feature diagnosis requirements. In addition, this study carried out image data processing through simulation, and designed comparative experiments to analyze the performance of the algorithm. The research shows that the FRFCM combined with the intuitionistic fuzzy set proposed in this paper has greatly improved the noise immunity and segmentation performance compared with the FCM based fuzzy set.

scholarly journals Test–Retest Reproducibility Analysis of Lung CT Image Features

2014 ◽  
Vol 27 (6) ◽  
pp. 805-823 ◽  
Author(s):  
Yoganand Balagurunathan ◽  
Virendra Kumar ◽  
Yuhua Gu ◽  
Jongphil Kim ◽  
Hua Wang ◽  
...  
Keyword(s):  
Image Features ◽  
Ct Image ◽  
Lung Ct

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

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