Tuberculin skin test result detection method based on CSN-II and improved OTSU method

Abstract

The tuberculin skin test (TST) is an intradermal test used to diagnose Type IV hypersensitivity reactions caused by Mycobacterium tuberculosis infection. Segmentation of TST result images provides a foundation for large-scale tuberculosis screening and auxiliary diagnosis. This paper presents a specialized method for identifying TST results. Initially, a clustering approach is employed to reduce pixel complexity, followed by a linear transformation using CSN-II to enhance the original RGB space with robust color space properties. Subsequently, high-probability pixel points are located, and their Gaussian kernel convolution range is determined using the Bhattacharyya Distance. Through convolution and iterative feature amplification, the target characteristics are progressively enhanced. Finally, an improved OTSU method is proposed for segmenting TST result images. In this method, an adaptive entropy threshold is utilized to reduce the search range of the OTSU method, enhancing the relative contrast between the target and the background. Moreover, a weighted adjustment is applied to the obtained OTSU threshold to prevent drift towards backgrounds with larger intra-class variances. Experimental results demonstrate that the proposed method achieves higher segmentation accuracy and robustness in TST result image segmentation compared to traditional OTSU methods and other improved approaches, such as the neighborhood valley-emphasis method, logarithmic OTSU, and weighted OTSU, Finally, we calculate a relative value is calculated by dividing the remaining number of segmented pixels by the total number of pixels, we then classify the results based on the relative value and in reference to medical diagnostic standards our method is intended to establish an algorithmic basis for rapid screening and classification of tuberculosis on a large scale.