Random forest-based tuberculosis bacteria classification in images of ZN-stained sputum smear samples
The World Health Organization suggests visual examination of stained sputum smear samples as a preliminary and basic diagnostic technique for diagnosing tuberculosis. The visual examination process requires much time of laboratorian, and also, it is prone to mistakes. For this purpose, this paper proposes a novel random forest (RF)-based segmentation and classification approaches for the automated classification of Mycobacterium tuberculosis in microscopic images of ZiehlNeelsen-stained sputum smears obtained using a light-field microscope. The RF supervised learning method is improved to classify each pixel depending on local color distributions as a part of candidate bacilli regions. Therefore, each pixel is labeled as either a candidate tuberculosis (TB) bacilli pixel or not. The candidate pixels are grouped together using connected component analysis. Each pixel group is then rotated, resized and centrally positioned within a bounding box, respectively, in order to utilize appearance-based tuberculosis bacteria identification algorithms. Finally, each region is classified by using the proposed RF learning algorithm trained on manually marked TB bacteria regions in the training images. The algorithm produces results that agree well with manual segmentation and identification. Different two-class pixel and object classifiers are also compared to show the performance of the proposed RF-based pixel segmentation and bacilli objects identification algorithm. The sensitivity and specificity of the proposed classifier are above 75.77 and 96.97% for the segmentation of the pixels, respectively. It is also revealed that the sensitivity increases over 93% when the staining is performed in accordance with the procedure. Moreover, these measures are above 89.34 and 62.89% for the identification of bacilli objects. The results show that the proposed novel method is quite successful when compared to the other applied methods.
random forests mycobacterium tuberculosis microscopic imaging pattern recognition support vector machines