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

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2019

Год издания: 2019

Идентификатор DOI: 10.1016/j.procs.2019.09.314

Ключевые слова: balance contrast enhancement technique, BCET, Chest X-ray imaging, gray level co-occurrence matrix, lung boundary detection, probabilistic neural network

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.

Ссылки на полный текст

Издание

Журнал: Procedia Computer Science

Выпуск журнала: Vol. 159

Номера страниц: 1439-1448

ISSN журнала: 18770509

Издатель: Elsevier B.V.

Авторы

  • Zotin A. (Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky rabochy av., Krasnoyarsk, 660037, Russian Federation)
  • Hamad Y. (Siberian Federal University, 79 Svobodny st., Krasnoyarsk, 660041, Russian Federation)
  • Simonov K. (Institute of Computational Modeling SB RAS, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation)
  • Kurako M. (Siberian Federal University, 79 Svobodny st., Krasnoyarsk, 660041, Russian Federation)

Вхождение в базы данных

Информация о публикациях загружается с сайта службы поддержки публикационной активности СФУ. Сообщите, если заметили неточности.

Вы можете отметить интересные фрагменты текста, которые будут доступны по уникальной ссылке в адресной строке браузера.