TY - GEN
T1 - Computer aided diagnosis workstation for brain tumor assessment
AU - Kawa, Jacek
AU - Rudzki, Marcin
AU - Pietka, Ewa
AU - Szwarc, Paweł
N1 - Publisher Copyright:
Copyright © 2015 Department of Microelectronics and Computer Science, Lodz Univeristy of Technology.
PY - 2015/8/17
Y1 - 2015/8/17
N2 - The paper presents a Computer Aided Diagnosis (CAD) software for brain tumor detection and analysis from Magnetic Resonance Imaging (MRI). The software utilizes a novel multi-stage method that is capable of dealing with three main types of brain tumors, i.e. HG gliomas, metastases and meningiomas and yields object masks as well as quantitative parameters. The processing method makes use of several available in MRI series: FLAIR, T1-Weighted, Contrast Enhanced T1-Weighted and Perfusion Weighted Images. Image processing methods involve registration of MR series, Region of Interest determination, multi-stage image segmentation, and finally analysis of neovasculature in suspected areas basing on perfusion maps. The brain regions are then labeled as normal, tumor or peritumoral. Relative perfusion coefficient is calculated with respect to a healthy white matter of the brain. Obtained results are presented to the radiologist. The presented CAD workstation is able to communicate with PACS (Picture Archiving and Communication System) using DICOM protocol.
AB - The paper presents a Computer Aided Diagnosis (CAD) software for brain tumor detection and analysis from Magnetic Resonance Imaging (MRI). The software utilizes a novel multi-stage method that is capable of dealing with three main types of brain tumors, i.e. HG gliomas, metastases and meningiomas and yields object masks as well as quantitative parameters. The processing method makes use of several available in MRI series: FLAIR, T1-Weighted, Contrast Enhanced T1-Weighted and Perfusion Weighted Images. Image processing methods involve registration of MR series, Region of Interest determination, multi-stage image segmentation, and finally analysis of neovasculature in suspected areas basing on perfusion maps. The brain regions are then labeled as normal, tumor or peritumoral. Relative perfusion coefficient is calculated with respect to a healthy white matter of the brain. Obtained results are presented to the radiologist. The presented CAD workstation is able to communicate with PACS (Picture Archiving and Communication System) using DICOM protocol.
KW - Computer Aided Diagnosis
KW - Magnetic Resonance Imaging
KW - brain tumour
KW - image processing
KW - perfusion maps
UR - https://www.scopus.com/pages/publications/84953744750
U2 - 10.1109/MIXDES.2015.7208489
DO - 10.1109/MIXDES.2015.7208489
M3 - Conference contribution
AN - SCOPUS:84953744750
T3 - Proceedings of the 22nd International Conference Mixed Design of Integrated Circuits and Systems, MIXDES 2015
SP - 98
EP - 103
BT - Proceedings of the 22nd International Conference Mixed Design of Integrated Circuits and Systems, MIXDES 2015
A2 - Napieralski, Andrzej
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference Mixed Design of Integrated Circuits and Systems, MIXDES 2015
Y2 - 25 June 2015 through 27 June 2015
ER -