Image Compression and Storage Algorithm of Histopathology Based on Matrix Calculation

To evaluate the clinical application value of the compression and storage algorithm of histopathological images based on matrix computing, and to seek the best image compression ratio.

Two classical matrix algorithms, principal component analysis (PCA) and singular value decomposition (SVD), were used to compress and reconstruct the immunohistochemical images and HE staining images of low, medium and high differentiated cervical cancer tissues. The peak signal-to-noise ratio(PSNR) and structural similarity (SSIM) were used to analyze and evaluate the quality of image reconstruction.

When the compression ratio of PCA reconstruction image was 10.18 (53 principal components were retained), the mean PSNR of immunohistochemical images of low, medium and high differentiated cervical cancer tissues were 43.84±0.43, 43.27±0.25 and 43.71±0.49, respectively, and the SSIM were 0.964±0.004, 0.963±0.006 and 0.965±0.005, respectively. Meanwhile, the mean PSNR of HE staining images of low, medium and high differentiated cervical cancer tissues were 43.41±0.78, 42.95±1.03 and 43.52±0.69, respectively, and the SSIM were 0.953±0.010, 0.949±0.015 and 0.960±0.007, respectively. When the compression ratio of SVD reconstruction image was 10.00(128 singular values were retained), the mean PSNR of immunohistochemical images of low, medium and high differentiated cervical cancer tissues were 39.89±1.69, 38.20±2.19 and 40.90±0.50, respectively, and the SSIM were 0.949±0.006, 0.938±0.011 and 0.955±0.004, respectively. Meanwhile, the mean PSNR of HE staining images of low, medium and high differentiated cervical cancer tissues were 40.31±0.98, 39.46±1.59 and 40.77±1.67, respectively, and the SSIM were 0.965±0.006, 0.943±0.010 and 0.969±0.005, respectively.

PCA and SVD can compress and store histopathological images and obtain better image quality, which provides a solution to the problem of hospital image storage.