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Adaptive voxel-based classifier for semi-automatic segmentation of tumors in the head and neck area based on T2-weighted MRI image data

Adaptives Voxel-Klassifizierungsverfahren zur semi-automatischen Segmentierung von Karzinomen auf Basis von T2-gewichteten Magnetresonanztomographie Bilddaten
: Appelhans, Lukas
: Sakas, Georgios; Jung, Florian

Darmstadt, 2015, 46 pp.
Darmstadt, TU, Bachelor Thesis, 2015
Bachelor Thesis
Fraunhofer IGD ()
medical image processing; medical imaging; segmentation; magnetic resonance imaging (MRI)

Measuring the size and location of a tumor is a major part of cancer staging and thus also crucial to plan treatment and predict the success chances of the same. Both properties can be extracted from a segmentation.
We present a new method for semi-automatic segmentation of tumors in the head and neck area using MR images. The new method incorporates known segmentations that were manually created by medical doctors. Other than that the only user interaction needed is setting a seed point.
After the seed point and an input image are entered, the algorithm starts by searching for a similar one in the database. The underlying assumption is that the intensities of a tumor in two comparable images also have comparable values. Using histograms for both the database image as well as the manual segmentation of it, the intensities that are likely to be featured in the tumor are calculated. After creating a basic segmentation, the actual tumor is extracted using opening, closing and a connected threshold filter.
The algorithm was developed using five datasets of T2-weighted MR images with a leave-one-out cross validation technique. When comparing the generated tumor segmentations with the manual ones, they had a DSC in the range of 0.41 and 0.77, with an average of 0.60. Furthermore the new method was also tested on lymph nodes. Further suggestions for improvements are given.