This invention discloses a method and
system for detecting abdominal
ultrasound in pets based on
image analysis, belonging to the field of pet abdominal
ultrasound image processing technology. This method maps the
ultrasound image to the complex
frequency domain using a two-dimensional
fast Fourier transform, extracting the spatial
phase spectrum. Using the zero-frequency center point as the origin of the emission point, low-frequency components of the
solid organ contours are filtered out. Non-axial
radial sampling rays are traversed within a preset high-
frequency band, extracting the phase value sequence and calculating the autocorrelation coefficient. The feature
direction angle corresponding to the maximum value is retrieved. A two-dimensional
Gaussian filter
mask is constructed based on this, and it is multiplied element-wise with a two-dimensional complex
frequency domain matrix to specifically reduce physical grid stripe
noise in that direction. Finally, a two-dimensional inverse
Fourier transform is used to restore the image to the
spatial domain and reconstruct an edge suppression feature map, which is then input into a
machine learning model to output an auxiliary probability report of the
lesion. This invention specifically filters out structured physical grid
noise caused by residual
hair roots in pets, significantly improving the accuracy of
lesion identification.