Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform

Disjoint subgraphs enclosed by the boundaries on the LDF landscape have high data significance and similarity and form an aware feature map. (1) to delineate the KEDF landscape of an input brain tumor image (I\in {\mathbb{R}}^{H\times W}) acquired from an open-source dataset33. In the last step (Step 4), segmentation of the candidate tumor image is achieved automatically using the characteristics of pixel connectivity. On the other hand, the border discontinuity of the PEDF landscape results in the geometric deformation of the LDF landscape. The parameters ({t}{i}\in \left{\mathrm{0,1}\right}) and ({g}{i}\in \left{\mathrm{0,1}\right}) are the binary tumor image segmentation prediction and ground truth labeling, respectively.