“Terrace compression method” and its application in heterogeneity contour detection of transmission images

Abstract

In multispectral transmission images, the strong scattering characteristics of biological tissues make the acquired signal weak and the image blurred, which brings difficulties to the contour detection of heterogeneity. The precision of image data can be improved by the modulation–demodulation technique, but the improvement of data effective bits also brings data redundancy. This paper proposes the “terrace compression method” and applies it to the detection of heterogeneity contours in transmission images. Experiments are designed to demonstrate the effectiveness of the proposed method. Four types of LEDs with different center wavelengths loaded with sinusoidal signals are used as the illumination sources respectively to acquire image sequences, and four single-wavelength images are demodulated by fast Fourier transform. The grayscale values of the demodulated images are sorted from low to high, several grayscale hard thresholds are set based on the quantity threshold obtained from the minimum size of the predicted detection target, and the grayscale values are divided into different intervals based on the grayscale hard thresholds. Then, the grayscale values below the threshold in each interval are combined to the minimum value of that interval. Finally, the Sobel operator is used to extract the heterogeneity contours. This method can realize image filtering, reduce data redundancy and enhance image gradient, which provides a new idea for heterogeneity contour detection of multispectral transmission images.