Neuro-levelset system based segmentation in dynamic susceptibility contrast enhanced and diffusion weighted magnetic resonance images

In this study, neuro-levelset method is proposed and evaluated for segmentation and grading of brain tumors on reconstructed images of dynamic susceptibility contrast (DSC) and diffusion weighted (DW) magnetic resonance images. The proposed neuro-levelset method comprises of two independent phases of processing. At first, reconstructed images have been independently processed by three different artificial neural network systems such as multilayer perceptron (MLP), self-organizing map (SOM), and radial basis function (RBF). The images used for these tasks were the cerebral blood volume (CBV), time to peak (TTP), percentage of base at peak (PBP) and apparent diffusion coefficient (ADC) images. This processing step ensued in formation of segmentation images of brain tumors. Further, in the second phase, these coarse segmented images of each artificial neural network system have been independently subjected as speed images to levelset method in order to optimize the segmentation performance. This has resulted in construction of three distinct neuro-levelset methods such as MLP-levelset, SOM-levelset and RBF-levelset method. Proposed neuro-levelset methods performed better in segmenting tumor, edema, necrosis, CSF and normal tissues as compared to independent artificial neural network systems. Among three neuro-levelset methods, RBF-levelset system has performed well with average sensitivity and specificity values of 91.43±2.94% and 94.43±1.90%, respectively.