Automatic window width and window level extraction method based on neural network

Abstract

The invention discloses an automatic window width and window level extraction method based on neural network. The automatic window width and window level extraction method classifies MR (magnetic resonance) images by utilizing an adaptive K clustering method, and comprises the following online training steps of (e) extracting histogram characters and space information characters of new MR images; (f) according to the histogram characters and the space information characters of the new MR images, classifying the new MR images by utilizing the adaptive K clustering method; and (g) comparing each class of the classified new MR images with each class of the current trained images, and firstly calculating the similarity of each class of the images with each class of the current trained images; if no similarity exits, adding a new class based on the original classes; and if the similarity exists, judging whether the window width and the window level of the class of the images are same as the golden standards of the window width and the window level of the class of the current trained images or not; if the window width and the window level of the class of the images are same as the golden standards of the window width and the window level of the class of the current trained images, not adding the new class; and if the window width and the window level of the class of the images are not same as the golden standards of the window width and the window level of the class of the current trained images, adding a new class based on the original classes. According to the automatic window width and window level extraction method based on neural network, which is disclosed by the invention, the online training can be automatically realized.

Description

technical field [0001] The invention relates to a medical image processing technology, in particular to a neural network-based automatic window width and window level extraction method. Background technique [0002] At present, the neural network is still relatively seldom used in the automatic window width and window level extraction method. Paper 1 (Ohliashi A, Yamada S, Haruki K, Hatano H, Fujii Y, Yamaguchi K, Ogata H. Automatic adjustment of display window for MR images using a neural network, Proceeding of SPIE, vol.1444, Image Capture, Formatting and Display, 1991.p.63-74) only uses a neural network, which cannot cover all kinds of MR images well. Paper 2 (Lai SH, Fang M.A hierarchical neural network algorithm for robust and automatic windowing of MR images. Artif Intell Med 2000; 19(2): 97-119) uses two layers of neural network, such as figure 1 Shown, comprise the following steps: Step S101, load MR image; Step S102, extract the histogram feature and spatial infor...

AI Technical Summary

Problems solved by technology

[0003] It can be seen that the existing neural network-based automatic window width and level extraction technology needs to retrain the entire neural network when encountering a new MR image sequence, or when the window width and level of a certain part of the image sequence change, which is inconvenient to use and affect efficiency

Images