  Multispectral transmission imaging provides strong benefits for early breast
cancer screening. The frame accumulation method addresses the challenge of low
grayscale and signal-to-noise ratio resulting from the strong absorption and
scattering of light by breast tissue. This method introduces redundancy in data
while improving the grayscale and signal-to-noise ratio of the image. Existing
terraced compression algorithms effectively eliminate the data redundancy
introduced by frame accumulation but necessitate significant time for manual
debugging of threshold values. Hence, this paper proposes an improved terrace
compression algorithm. The algorithm necessitates solely the input of the
desired heterogeneous body size and autonomously calculates the optimal area
threshold and gradient threshold by counting the grayscale and combining its
distribution. Experimental acquisition involved multi-wavelength images of
heterogeneous bodies exhibiting diverse textures, depths, and thicknesses.
Subsequently, the method was applied after pre-processing to determine the
thresholds for terraced compression at each wavelength, coupled with a window
function for multi-dimensional image clustering. The results illustrate the
method's efficacy in detecting and identifying various heterogeneous body
types, depths, and thicknesses. This approach is expected to accurately
identify the locations and types of breast tumors in the future, thus providing
a more dependable tool for early breast cancer screening.
