Improving the sub-cortical gm segmentation using evolutionary hierarchical region merging

Çiftçioğlu, Mustafa Ulaş
Segmentation of sub-cortical Gray Matter (GM) structures in magnetic resonance brain images is crucial in clinic and research for many purposes such as early diagnosis of neurological diseases, guidance of surgical operations and longitudinal volumetric studies. Unfortunately, the algorithms that segment the brain into 3 tissues usually suffer from poor performance in the sub-cortical region. In order to increase the detection of sub-cortical GM structures, an evolutionary hierarchical region merging approach, abbreviated as EHRM, is proposed in this study. Through EHRM, an intensity based region merging is utilized while merging is allowed to proceed among disconnected regions. Texture information is also incorporated into the scheme to prevent the region merging between tissues with similar intensity but different texture properties. The proposed algorithm is tested on real and simulated datasets. The performance is compared with a popular segmentation algorithm, which is also intensity driven: the FAST algorithm [1] in the widely used FSL suite. EHRM is shown to make a significant improvement the detection of sub-cortical GM structures. Average improvements of 10%, 36% and 22% are achieved for caudate, putamen and thalamus respectively. The accuracy of volumetric estimations also increased for GM and WM. Performance of EHRM is robust in presence of bias field. In addition, EHRM operates in O(N) complexity. Furthermore, the algorithm proposed here is simple, because it does not incorporate spatial priors such as an atlas image or intensity priors. With these features, EHRM may become a favorable alternative to the existing brain segmentation tools.


Statistical disease detection with resting state functional magnetic resonance imaging
Öztürk, Ebru; İlk Dağ, Özlem; Department of Statistics (2017)
Most of the functional magnetic resonance imaging (fMRI) data are based on a particular task. The fMRI data are obtained while the subject performs a task. Yet, it's obvious that the brain is active even when the subject is not performing a task. Resting state fMRI (R-fMRI) is a comparatively new and popular technique for assessing regional interactions when a subject is not performing a task. This study focuses on classifying subjects as healthy or diseased with the diagnosis of schizophrenia by analyzing ...
Representation of human brain by mesh networks
Önal Ertuğrul, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this thesis, we propose novel representations to extract discriminative information in functional Magnetic Resonance Imaging (fMRI) data for cognitive state and gender classification. First, we model the local relationship among a set of fMRI time series within a neighborhood by considering temporal information obtained from all measurements in time series. The estimated local relationships, called Mesh Arc Descriptors (MADs), are employed to represent information in fMRI data. Second, we adapt encoding ...
Measurement of AC magnetic field distribution using magnetic resonance imaging
Ider, YZ; Muftuler, LT (1997-10-01)
Electric currents are applied to body in numerous applications in medicine such as electrical impedance tomography, cardiac defibrillation, electrocautery, and physiotherapy. If the magnetic field within a region is measured, the currents generating these fields can be calculated using the curl operator. In this study, magnetic fields generated within a phantom by currents passing through an external wire is measured using a magnetic resonance imaging (MRI) system, A pulse sequence that is originally design...
A medical image processing and analysis framework
Çevik, Alper; Eyüboğlu, Behçet Murat; Oğuz, Kader Karlı; Department of Biomedical Engineering (2011)
Medical image analysis is one of the most critical studies in field of medicine, since results gained by the analysis guide radiologists for diagnosis, treatment planning, and verification of administered treatment. Therefore, accuracy in analysis of medical images is at least as important as accuracy in data acquisition processes. Medical images require sequential application of several image post-processing techniques in order to be used for quantification and analysis of intended features. Main objective...
Analysis of magnetic resonance imaging in inhomogenous main magnetic field
Arpınar, Volkan Emre; Eyüboğlu, Behçet Murat; Department of Electrical and Electronics Engineering (2009)
In this study, analysis of Magnetic Resonance Imaging (MRI) in inhomogeneous main magnetic field is conducted. A numerical model based on Bloch equation is implemented for MRI, to understand effect of inhomogeneous magnetic field to Magnetic Resonance (MR) signal. Using the model, relations between inhomogeneity levels in main magnetic field with energy, decay time, bandwidth of the FID signal is investigated. Also relation between the magnetic field inhomogeneity and field of view is determined. To simulat...
Citation Formats
M. U. Çiftçioğlu, “Improving the sub-cortical gm segmentation using evolutionary hierarchical region merging,” M.S. - Master of Science, Middle East Technical University, 2011.