Three learning phases for radial-basis-function networks
Neural Networks
Fast Evolution of Image Manifolds and Application to Filtering and Segmentation in 3D Medical Images
IEEE Transactions on Visualization and Computer Graphics
Image Segmentation by Networks of Spiking Neurons
Neural Computation
Segmentation of brain tumors in 4D MR images using the hidden Markov model
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Segmenting Brain Tumors Using Pseudo---Conditional Random Fields
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Level-set segmentation of brain tumors using a threshold-based speed function
Image and Vision Computing
Novel maximum-margin training algorithms for supervised neural networks
IEEE Transactions on Neural Networks
Representing diffusion MRI in 5d for segmentation of white matter tracts with a level set method
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Brain segmentation with competitive level sets and fuzzy control
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review
IEEE Transactions on Information Technology in Biomedicine
A unified approach to noise removal, image enhancement, and shape recovery
IEEE Transactions on Image Processing
A pyramid approach to subpixel registration based on intensity
IEEE Transactions on Image Processing
| Hi-index | 0.01 |
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.