The article considers the application of criteria for assessing the quality of segmentation of digital images of sputum stained by the method of Ziehl–Neelsen to select the optimal parameter "Sigma" wavelet transform Mexican Hat. 830 digital images obtained by sputum smear microscopy were used as the study material. To assess the optimal selection of the parameter σ, we used the average number of objects selected in the images, the proportion of missed acid-resistant mycobacteria in the images, the homogeneity criterion and 3 complex criteria for assessing the quality of image segmentation. The analysis showed that with an increase in the parameter σ there is a slight decrease in the value of the homogeneity criterion. At the same time, the parameter σ increases from 2.4 and more according to the complex criteria, and the image segmentation quality improves. Thus, the most optimal values of the σ parameter of the Mexican Hat wavelet for segmentation of digital images of sputum stained by the cyl-Nielsen method are values in the range from 2.90 to 3.09.
Keywords: method Ziehl-Nielsen, image segmentation, quality evaluation criteria, wavelet transform, Mexican Hat
A comparison of different methods of segmentation of digital images of sputum stained by the method of Ziehl-Nielsen. We considered the following methods: threshold binarization, method binarization Otsu, detectors borders (operators Roberts, Sobel, Prewitt, Robinson and Kenny), detectors of Harris corners and FAST (Features from Accelerated Segment Test) algorithm, artificial neural network and wavelet transform Mexican Hat, as well as the search function of the contours of the OpenCV library. To analyze the quality of the image segmentation and time spent for carrying out segmentation. Concluded that the use of the wavelet transform Mexican Hat has the best quality segmentation with a relatively small time spent.
Keywords: the method of Ziehl-Nielsen, segmentation, digital imaging, detector angles, FAST, operator Kenny, the Sobel operator, Roberts operator, the operator Prewitt, operator Robinson, artificial neural networks, OpenCV