Computer aided diagnosis workstation for brain tumor assessment

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.

[1]  Ewa Pietka,et al.  Granular computing in model based abdominal organs detection , 2015, Comput. Medical Imaging Graph..

[2]  Anne L. Martel,et al.  Extracting parametric images from dynamic contrast-enhanced MRI studies of the brain using factor analysis , 2001, Medical Image Anal..

[3]  Barbara Bobek-Billewicz,et al.  Multiseries MR Data in Brain Tumours Segmentation , 2010 .

[4]  Dominik Spinczyk,et al.  Role of radiologists in CAD life-cycle. , 2011, European journal of radiology.

[5]  Pawel Badura,et al.  Soft computing approach to 3D lung nodule segmentation in CT , 2014, Comput. Biol. Medicine.

[6]  Stephen T. C. Wong,et al.  Pice: Prior information constrained evolution for 3-D and 4-D brain tumor segmentation , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[7]  W. Wieclawek.  Live-wire Method With FCM Classification , 2006, Proceedings of the International Conference Mixed Design of Integrated Circuits and System, 2006. MIXDES 2006..

[8]  Ewa Pietka,et al.  White Matter Segmentation from MR Images in Subjects with Brain Tumours , 2012, ITIB.

[9]  Leo Joskowicz,et al.  Automatic segmentation of Optic Pathway Gliomas in MRI , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[10]  Shan Hu,et al.  Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis. , 2014, Bio-medical materials and engineering.

[11]  Pawel Badura,et al.  Open architecture computer‐aided diagnosis system , 2010, Expert Syst. J. Knowl. Eng..

[12]  Christopher Nimsky,et al.  A Medical Software System for Volumetric Analysis of Cerebral Pathologies in Magnetic Resonance Imaging (MRI) Data , 2012, Journal of Medical Systems.

[13]  Nico Karssemeijer,et al.  Using computer-aided detection in mammography as a decision support , 2010, European Radiology.

[14]  Jeon-Hor Chen,et al.  Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images , 2013, IEEE Transactions on Medical Imaging.

[15]  E. Pietka,et al.  Quantitative tumour tissue measurements in subjects with high-grade gliomas , 2011, Proceedings of the 18th International Conference Mixed Design of Integrated Circuits and Systems - MIXDES 2011.

[16]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[17]  Nelly Gordillo Castillo,et al.  A new fuzzy approach to brain tumor segmentation , 2010, International Conference on Fuzzy Systems.

[18]  H. K. Huang,et al.  Computer-Assisted Bone Age Assessment: Graphical User Interface for Image Processing and Comparison , 2004, Journal of Digital Imaging.

[19]  Piotr Zarychta.  ACL and PCL of the knee joint in the computer diagnostics , 2014, 2014 Proceedings of the 21st International Conference Mixed Design of Integrated Circuits and Systems (MIXDES).

[20]  Pawel Badura,et al.  4D Segmentation of Ewing’s Sarcoma in MR Images , 2010 .

[21]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[22]  Francisco Jesús Martínez-Murcia,et al.  LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease , 2013, Pattern Recognit. Lett..

[23]  Ruzena Bajcsy,et al.  Unsupervised segmentation of brain tissue in multivariate MRI , 2010, ISBI.

[24]  William E. Brant,et al.  Fundamentals of Diagnostic Radiology , 1999 .

[25]  M. Ozkan,et al.  Evaluation of Hemodynamic Perfusion MR Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.