Processing of CT Lung Images as a Part of Radiomics

Зотин, А. Г.; Hamad, Yousif; Simonov, Konstantin; Kurako, Mikhail; Kents, Anzhelika

doi:10.1007/978-981-15-5925-9_21

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The scale and consistency of the results were defined by the structural components used in these procedures. In the morphological opening phase, smaller front structures of architectural members were removed, while in the morphological process, smaller back structures of structural components were often eliminated [8] [9].…”

Section: Methodsmentioning

confidence: 99%

Infusion extraction and measurement on CT images based on computer vision and neural network

Hamad,

Alekhina,

Pleshkova

et al. 2024

BIO Web Conf.

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This paper presents a new approach to the automated detection and quantification of pulmonary emphysema and pneumoconiosis using computed tomography images. The proposed method employs computer vision and neural network algorithms to improve the accuracy and speed of lung diagnosis, as well as the monitoring of emphysema and its changes over time. The study analyzes existing approaches and demonstrates the novelty of the proposed method. The paper reports high accuracy of emphysema extraction and size measurements based on three different patient cases, as evaluated by an expert, and the successful segmentation of pneumosclerosis. The proposed method has the potential to significantly improve medical image segmentation, particularly in the detection and diagnosis of diseases such as Chronic Obstructive Pulmonary Disease (COPD) and COVID-19. The study concludes that the proposed method may also be useful in other areas of medical imaging, contributing to the ongoing effort to develop new and improved methods for medical image analysis and interpretation.

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Section: Methodsmentioning

confidence: 99%

Infusion extraction and measurement on CT images based on computer vision and neural network

Hamad,

Alekhina,

Pleshkova

et al. 2024

BIO Web Conf.

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“…The center pixel is labeled by thresholding its value, multiplying it by the power of two, and then stringing the results together. Since the area around the center consists of 8 pixels, there are 2 8 = 256 different labels that may be constructed by looking at the gray levels of both the center and the surrounding region. The central tendency and dispersion of the LBP features of an image are also considered during classification.…”

Section: Lbp Featuresmentioning

confidence: 99%

“…The selection of informative training and testing samples is also difficult [6,7]. Amin et al [8] developed a novel method of MR brain categorization. Gaussian-filtered images of the brain were processed further to remove any remaining noise.…”

Section: Introductionmentioning

confidence: 99%

Brain Pathology Classification of MR Images Using Machine Learning Techniques

Ramaha,

Mahmood,

Hameed

et al. 2023

Computers

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A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method’s efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%.

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