  In this paper, we propose a system to detect brain tumor in 3D MRI brain
scans of Flair modality. It performs 2 functions: (a) predicting gray-level and
locational distributions of the pixels in the tumor regions and (b) generating
tumor mask in pixel-wise precision. To facilitate 3D data analysis and
processing, we introduced a 2D histogram presentation that comprehends the
gray-level distribution and pixel-location distribution of a 3D object. In the
proposed system, particular 2D histograms, in which tumor-related feature data
get concentrated, are established by exploiting the left-right asymmetry of a
brain structure. A modulation function is generated from the input data of each
patient case and applied to the 2D histograms to attenuate the element
irrelevant to the tumor regions. The prediction of the tumor pixel distribution
is done in 3 steps, on the axial, coronal and sagittal slice series,
respectively. In each step, the prediction result helps to identify/remove
tumor-free slices, increasing the tumor information density in the remaining
data to be applied to the next step. After the 3-step removal, the 3D input is
reduced to a minimum bounding box of the tumor region. It is used to finalize
the prediction and then transformed into a 3D tumor mask, by means of gray
level thresholding and low-pass-based morphological operations. The final
prediction result is used to determine the critical threshold. The proposed
system has been tested extensively with the data of more than one thousand
patient cases in the datasets of BraTS 2018~21. The test results demonstrate
that the predicted 2D histograms have a high degree of similarity with the true
ones. The system delivers also very good tumor detection results, comparable to
those of state-of-the-art CNN systems with mono-modality inputs, which is
achieved at an extremely low computation cost and no need for training.
