A Novel Prediction Model for Brain Glioma Image Segmentation Based on the Theory of Bose-Einstein Condensate - Zhang, Tian Chi; Zhang, Jing; Chen, Shou Cun; Saada, Bacem (Frontiers in Medicine, 2022)
Background: The input image of a blurry glioma image segmentation is, usually, very unclear. It is difficult to obtain the accurate contour line of image segmentation. The main challenge facing the researchers is to correctly determine the area where the points on the contour line belong to glioma image. This article highlights the mechanism of formation of glioma and provides an image segmentation prediction model to assist in the accurate division of glioma contour points. The proposed prediction model of segmentation associated to the process of the formation of glioma is innovative and challenging. Bose-Einstein Condensate (BEC) is a microscopic quantum phenomenon in which atoms condense to the ground state of energy as the temperature approaches absolute zero. In this article, we propose a BEC kernel function, and a novel prediction model based on BEC kernel to detect the relationship between the process of the BEC and the formation of a brain glioma. Furthermore, the theory derivation and proof of the prediction model are given from micro to macro through quantum mechanics, wave, oscillation of glioma and statistical distribution of laws. The prediction model is a distinct segmentation model that is guided by BEC theory for blurry glioma image segmentation. Results: Our approach is based on five tests; the first three tests aim at confirming the measuring range of T and μ in the BEC kernel. The results are extended from -10 to 10, approximating the standard range to T <=0. And μ from 0 to 6.7. Test 4 and test 5 are comparison tests. Test 4 comparison is based on various established cluster methods. The results show that our prediction model in image evaluation parameters of P, R, and F is the best amongst all existent ten forms except for only one reference with the mean value of F that is between 0.88 and 0.93, while our approach returns between 0.85 and 0.99.