3D texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images

2013 ◽  
Author(s):  
Ashis Kumar Dhara ◽  
Sudipta Mukhopadhyay ◽  
Niranjan Khandelwal
Keyword(s):  
Texture Analysis ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Solitary Pulmonary Nodules ◽  
3D Texture ◽  
3D Texture Analysis ◽  
Occurrence Matrix ◽  
Lung Ct

Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images

2013 ◽  
Author(s):  
Ashis Kumar Dhara ◽  
Sudipta Mukhopadhyay ◽  
Naved Alam ◽  
Niranjan Khandelwal
Keyword(s):  
Pulmonary Nodules ◽  
Ct Images ◽  
Solitary Pulmonary Nodules ◽  
Lung Ct

scholarly journals 3D texture analysis for classification of second harmonic generation images of human ovarian cancer

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Bruce Wen ◽  
Kirby R. Campbell ◽  
Karissa Tilbury ◽  
Oleg Nadiarnykh ◽  
Molly A. Brewer ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Second Harmonic Generation ◽  
Texture Analysis ◽  
Harmonic Generation ◽  
Second Harmonic ◽  
Human Ovarian Cancer ◽  
3D Texture ◽  
3D Texture Analysis

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

Reliability of 3D texture analysis: A multicenter MRI study of the brain

2019 ◽  
Vol 51 (4) ◽  
pp. 1200-1209 ◽  
Author(s):  
Daniel Ta ◽  
Muhammad Khan ◽  
Abdullah Ishaque ◽  
Peter Seres ◽  
Dean Eurich ◽  
...  
Keyword(s):  
Texture Analysis ◽  
3D Texture ◽  
3D Texture Analysis ◽  
Mri Study ◽  
The Brain

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.

Quantitative evaluation of margin sharpness of pulmonary nodules in lung CT images

2016 ◽  
Vol 10 (9) ◽  
pp. 631-637 ◽  
Author(s):  
Ashis Kumar Dhara ◽  
Satrajit Chakrabarty ◽  
Niranjan Khandelwal ◽  
Mandeep Garg ◽  
Sudipta Mukhopadhyay
Keyword(s):  
Quantitative Evaluation ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Lung Ct

3D Texture Analysis of Corpus Caliosum Based on MR Images Inpatients with Alzheimer’s Disease and Mild Cognitive Impairment

2014 ◽  
Vol 533 ◽  
pp. 415-420 ◽  
Author(s):  
Wei Fang Liu ◽  
Xu Wang ◽  
Hong Xia
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Texture Analysis ◽  
Three Dimensional ◽  
Texture Features ◽  
Grey Level ◽  
Run Length ◽  
3D Texture ◽  
3D Texture Analysis

This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimers disease (AD). Methods: T1-weighted MRI of 18 AD patients, 18 Mild Cognitive Impairment (MCI) patients and 18 normal controls (NC) were selected.3D Texture parameters of the corpus callosum,including contrast, inverse difference moment , entropy, short run emphasis, long run emphasis, grey level nonuniformity, run length nonuniformity and fraction were extracted from the gray level co-occurrence matrix and run length matrix. Finally statistic significance was tested among three groups, and the correlations between parameters and Mini-Mental State Examination (MMSE) scores were calculated. Results: The results showed that the 3D texture features had significant differences (p<0.05) among three groups except grey level nonuniformity and run length nonuniformity that the difference was not significant (p>0.05) between MCI and NC or AD and MCI , and they were correlated with MMSE scores.Conclusions: 3D texture analysis can reflect the pathological changes of corpus callosum in patients with AD and MCI, and it may be helpful to AD early diagnosis.

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