TY - GEN
T1 - Parallel processing of computed tomography images
AU - Połap, Dawid
AU - Woźniak, Marcin
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Medical research is not only expensive but also time-consuming, what can be seen in the queues, and then after the waiting time for the analysis of the effects obtained from tests. In the case of computed tomography examinations, the end result is a series of the described images of the examined object’s shape. The description is made on the careful observation of the results. In this work, we propose a solution that allows to select images that are suspicious. This type of technique reduces the amount of data that needs to be analyzed and thus reduces the waiting time for the patient. The idea is based on a three-stage data processing. In the first one, key-points are located as features of found elements, in the second, images are constructed containing found areas of images, and in the third, the classifier assesses whether the image should be analyzed in terms of diseases. The method has been described and tested on a large CT dataset, and the results are widely discussed.
AB - Medical research is not only expensive but also time-consuming, what can be seen in the queues, and then after the waiting time for the analysis of the effects obtained from tests. In the case of computed tomography examinations, the end result is a series of the described images of the examined object’s shape. The description is made on the careful observation of the results. In this work, we propose a solution that allows to select images that are suspicious. This type of technique reduces the amount of data that needs to be analyzed and thus reduces the waiting time for the patient. The idea is based on a three-stage data processing. In the first one, key-points are located as features of found elements, in the second, images are constructed containing found areas of images, and in the third, the classifier assesses whether the image should be analyzed in terms of diseases. The method has been described and tested on a large CT dataset, and the results are widely discussed.
KW - CT images
KW - Convolutional neural network
KW - Image processing
UR - https://www.scopus.com/pages/publications/85053284941
U2 - 10.1007/978-3-319-99996-8_9
DO - 10.1007/978-3-319-99996-8_9
M3 - Conference contribution
AN - SCOPUS:85053284941
SN - 9783319999951
T3 - Advances in Intelligent Systems and Computing
SP - 95
EP - 104
BT - Information Systems Architecture and Technology
A2 - Swiatek, Jerzy
A2 - Borzemski, Leszek
A2 - Wilimowska, Zofia
PB - Springer Verlag
T2 - 39th International Conference Information Systems Architecture and Technology, ISAT 2018
Y2 - 16 September 2018 through 18 September 2018
ER -