Interpretation of CT Lung images by the radiologist can be enhanced to a greater extent by automatic segmentation of nodules. The efficiency of this interpretation depends on the completeness and non-ambiguousness of the CT Lung images. Here, a fully automatic cascaded basis was proposed for CT Lung image segmentation. In this proposal a customized FCN was used feature extractions exploration from many visual scales and differentiate anatomy with a thick forecast map. Widespread experimental outcomes demonstrate that this technique can address the incompleteness in boundary and this technique can achieve best accuracy in segmentation of Lung CT Images when compared to other techniques which address the same area
Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization