Comparison of techniques for segmenting digital microscopic images of sputum stained by the method of Ziehl-Nielsen

Abstract

Comparison of techniques for segmenting digital microscopic images of sputum stained by the method of Ziehl-Nielsen

Narkevich A.N., Shelomenceva I.G., Vinogradov K.A., Sysoev S.A.

Incoming article date: 18.10.2017

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