Adaptive online learning based tissue segmentation of MR brain images


Master Thesis



Chris Damkat

Electrical Engineering

Eindhoven University of Technology


The aging population in the European Union and the US has increased the importance of research in neurodegenerative diseases. Imaging plays an essential role in this endeavor by providing insight to the intricate cellular and inter-cellular processes in living tissues that will otherwise be difficult, or impossible, to gain. Because of the sheer size of the imagery data, the lack of sufficient medical staff, and the inaccuracies resulting from manual processing, automated processing of image-based data to generate quantitative and reproducible results is necessary. To this effect, in this thesis a fully automatic image-processing algorithm for brain tissue segmentation from magnetic resonance (MR) images is proposed. Contrary to the present Expectation Maximization (EM) based algorithms, it uses online (sample-by-sample) learning to adapt to the intensity inhomogeneity inherent to MR images. Since the proposed method can adapt to the intensity inhomogeneity online, multiple iterations over the data as in the present algorithms are not necessary, and consequently the processing time is decreased dramatically. The used online learning scheme is based on Learning Vector Quantization and is further tailored to the segmentation of MR images by integration of spatial context and the use of a special scanning order of the data. Explorations of various scanning orders and a modification to the learning rule to allow for 3D learning have lead to three variants of the proposed algorithm. These proposed methods are validated by comparing the segmentation masks to basic k-means clustering, and present EM-based methods, namely, FAST and the state-of-the-art EMS, on simulated and real datasets. The proposed methods demonstrated a significant reduction of the processing time (a factor of 20) compared to the EM-based methods. Tests on simulated data showed that segmentation accuracy is comparable to the EM-based methods, however, tests on real data where the segmentations of EMS were used as ground truth showed lower performance than the EM-based FAST. Moreover, the tests on real data showed that the proposed methods as well as FAST make a significant amount of misclassifications in the so-called deep gray matter, which suggests the necessity of a spatial prior atlas as it is used in EMS.