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Найдено документов в текущей БД: 2

Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks

/ A. Zotin [et al.] // Procedia Computer Science : Elsevier B.V., 2019. - Vol. 159: 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2019 (4 September 2019 through 6 September 2019, ) Conference code: 141548. - P1439-1448, DOI 10.1016/j.procs.2019.09.314 . -

Кл.слова (ненормированные):
balance contrast enhancement technique -- BCET -- Chest X-ray imaging -- gray level co-occurrence matrix -- lung boundary detection -- probabilistic neural network -- Biological organs -- Computer aided diagnosis -- Image segmentation -- Knowledge based systems -- Neural networks -- Radiography -- BCET -- Boundary detection -- Chest x-rays -- Contrast Enhancement -- Gray level co-occurrence matrix -- Probabilistic neural networks -- Image classification

Аннотация: Extraction of various structures from the chest X-ray (CXR) images and abnormalities classification are often performed as an initial step in computer-aided diagnosis/detection (CAD) systems. The shape and size of lungs may hold clues to serious diseases such as pneumothorax, pneumoconiosis and even emphysema. With the growing number of patients, the doctors overwork and cannot counsel and take care of all their patients. Thus, radiologists need a CAD system supporting boundary CXR images detection and image classification. This paper presents our automated approach for lung boundary detection and CXR classification in conventional poster anterior chest radiographs. We extract the lung regions, sizes of regions, and shape irregularities with segmentation techniques that are used in image processing on chest radiographs. From CXR image we extract 18 features using the gray level co-occurrence matrix (GLCM). It allows us to classify the CXR image as normal or abnormal using the probabilistic neural network (PNN) classifier. The proposed method has competitive results with comparatively shorter training time and better accuracy. © 2019 The Author(s). Published by Elsevier B.V.

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Держатели документа:
Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky rabochy av., Krasnoyarsk, 660037, Russian Federation
Siberian Federal University, 79 Svobodny st., Krasnoyarsk, 660041, Russian Federation
Institute of Computational Modeling SB RAS, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation

Доп.точки доступа:
Zotin, A.; Hamad, Y.; Simonov, K.; Kurako, M.

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Lung Boundary Detection and Classification in Chest X-Rays Images Based on Neural Network

/ Y. A. Hamad, K. Simonov, M. B. Naeem // Communications in Computer and Information Science : Springer, 2020. - Vol. 1174 CCIS: 1st International Conference on Applied Computing Research to Support Industry: Innovation and Technology, ACRIT 2019; Ramadi; Iraq; 15 September 2019 through 16 September 2019; Code 235839. - P3-16, DOI 10.1007/978-3-030-38752-5_1 . -

Кл.слова (ненормированные):
Balance Contrast Enhancement Technique -- Chest X-ray imaging -- Classification -- Gray level co-occurrence matrix -- Lung boundary detection -- Probabilistic neural network -- Biological organs -- Classification (of information) -- Engineering research -- Image classification -- Image segmentation -- Matrix algebra -- Neural networks -- X ray radiography -- Boundary detection -- Chest x-rays -- Contrast Enhancement -- Gray level co-occurrence matrix -- Probabilistic neural networks -- Computer aided diagnosis

Аннотация: The isolation of different structures is often performed on chest radiography (CXR) and the classification of abnormalities is an initial step in detection systems as computer-aided diagnosis (CAD). The shape and size of lungs may hold clues to serious diseases such as pneumothorax, pneumoconiosis and even emphysema. More than 500,000 people die in the United States every year due to heart and lung failure, often being tested for the normal CXR film. With an increasing number of patients, the doctors must over-work, hence they cannot provide the advice and take care of their patients correctly. In this case, the computer system that supports image classification and boundary CXR detection is needed. This paper presents our automated approach for lung boundary detection and CXR classification in conventional poster anterior chest radiographs. We first extract the lung region, size measurements, and shape irregularities using segmentation techniques that are used in image processing on chest radiographs. For the CXR image, we extract 18 various features using the gray level co-occurrence matrix (GLCM) which enables the CXR to be classified as normal or abnormal using the probabilistic neural network (PNN) classifier. We measure the performance of our system using two data sets: the Montgomery County (MC) x-ray dataset and the Shenzhen X-ray dataset. The proposed methodology has competitive results with relatively shorter training time and higher accuracy. © 2020, Springer Nature Switzerland AG.

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Держатели документа:
Siberian Federal University, Academician Kirensky, 1st Building, Krasnoyarsk, Krasnoyarsk Krai, 660074, Russian Federation
Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Sciences, Akademgorodok Krasnoyarsk, Krasnoyarsk Krai, 660036, Russian Federation
Department of Computer Science, Al-Maarif University College, Ramadi, Anbar, 31001, Iraq

Доп.точки доступа:
Hamad, Y. A.; Simonov, K.; Naeem, M. B.

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