A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> FIGURE 22. <s> This paper aims at introducing a new approach to Plasmodium vivax (P. vivax) detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of two main stages - firstly artefacts reduction, and secondly fuzzy divergence based segmentation of P. vivax infected region(s) from erythrocytes. Here, malaria parasite segmentation is done using divergence based threshold selection. Fuzzy approach is chosen to minimize ambiguity inherent in the microscopic images. Divergence algorithm is derived from Cauchy membership function to overcome the drawbacks in comparison with other well known membership functions. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> FIGURE 22. <s> 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. <s> BIB002
Top part of the figure shows the original images and down part of the figure shows segmented malaria parasites from rest of the background using fuzzy divergence method BIB001 . Plasmodium vivax are used. The flow chart for segmentation algorithm is shown in Fig. 21 and segmentation results are shown in Fig. 22 . In BIB002 , evaluation of clustering and threshold segmentation techniques on tissue images containing TB bacilli is presented, where the performance of 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. 100 RGB images of TB positive sample tissues are used. These images are taken from variety of stained conditions termed as good, understained and overstained, then they are converted to C-Y color mode, and the saturation component is utilized as input to these algorithms, because it provides good separation between the TB bacilli and the background.