Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.88 ◽

2017 ◽

Vol 7 (7) ◽

pp. 8

Author(s):

Mona E. Elbashier ◽

Suhaib Alameen ◽

Caroline Edward Ayad ◽

Mohamed E. M. Gar-Elnabi

Keyword(s):

Size Distribution ◽

Texture Analysis ◽

Classification Accuracy ◽

Ct Images ◽

Diabetic Patients ◽

Gray Level ◽

Grey Level ◽

Run Length ◽

Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

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Fast fully automatic detection, classification and 3D reconstruction of pulmonary nodules in CT images by local image feature analysis

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102790 ◽

2021 ◽

Vol 68 ◽

pp. 102790

Author(s):

Chung-Feng Jeffrey Kuo ◽

Jagadish Barman ◽

Chia Wen Hsieh ◽

Hsian-He Hsu

Keyword(s):

3D Reconstruction ◽

Pulmonary Nodules ◽

Automatic Detection ◽

Ct Images ◽

Image Feature ◽

Feature Analysis ◽

Fully Automatic ◽

Local Image

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

Image Analysis & Stereology ◽

10.5566/ias.1679 ◽

2017 ◽

Vol 36 (2) ◽

pp. 65 ◽

Cited By ~ 7

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.

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Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

Frontiers in Public Health ◽

10.3389/fpubh.2021.671070 ◽

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.

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