Performance comparison of clustering and thresholding algorithms for tuberculosis bacilli segmentation

Abstract

Image segmentation is a key step in most medical image analysis. However, the process is particularly difficult due to limitation of the imaging equipments and variation in biological system. Therefore, accurate and robust segmentation are important for quantitative assessment of medical images in order to achieve correct clinical diagnosis. This paper studies the performance of clustering and adaptive thresholding algorithms for segmenting the tuberculosis (TB) bacilli in tissue sections. Images are obtained by analyzing the Ziehl-Neelsen (ZN) stained tissue slide and capturing using a digital camera attached to a light microscope. Three clustering algorithms namely k-mean clustering, moving k-mean clustering and fuzzy c-mean clustering, and two adaptive thresholding algorithms, Otsu and iterative thresholding, are evaluated for segmentation of TB bacilli. The saturation component, derived from C-Y colour model is utilised as input to these algorithms as it provides good separation between the TB bacilli and the background. The segmentation results are further compared with the manual-segmentation image. Acceptable segmentation accuracy of up to 99.00% was achieved by using the clustering algorithms and the Otsu's thresholding. However, k-mean clustering was chosen as it produced the highest TB segmentation rate.