A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. UNSUPERVISED MACHINE LEARNING (USML) <s> We describe a semi-automated method to segment multicolored microbes in digitized color images containing complex, noisy backgrounds. Instead of providing a simple threshold, the system uses an interactive environment whereby one samples multiple pixels to represent the range of color pixels comprising the foreground microbes of interest. The color and spatial distances of these target points are then used to segment the microbes from the confusing background. The new system should have wide applications in digital image analysis of multicolored objects in complex colored images, as is commonplace in studies of in-situ microbial ecology and biomedical imaging. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. UNSUPERVISED MACHINE LEARNING (USML) <s> The Self Organising Tree Map (SOTM) neural network is investigated as a means of segmenting micro-organisms from confocal microscope image data. Features describing pixel and regional intensities, phase congruency and spatial proximity are explored in terms of their impact on the segmentation of bacteria and other micro-organisms. The significance of individual features is investigated, and it is proposed that, within the context of micro-biological image segmentation, better object delineation can be achieved if certain features are more dominant in the initial stages of learning. In this way, other features are allowed to become more/less significant as learning progresses: as more knowledge is acquired about the data being segmented. We argue that the efficiency and flexibility of the SOTM in adapting to, and preserving the topology of input space, makes it an appropriate candidate for implementing this idea. We propose a refinement to the competitive search strategy that allows for a more appropriate fusion of signal and proximal features, thereby promoting a segmentation that is more sensitive to the regional associations of different microbial matter. A refined stop criterion is also suggested such that the dynamically generated number of classes becomes more data dependant. Preliminary experiments are presented and it is found that favouring intensity characteristics in the early phases of learning, whilst relaxing proximity constraints in later phases of learning, offers a general mechanism through which we can improve the segmentation of microbial constituents'. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. UNSUPERVISED MACHINE LEARNING (USML) <s> The roundworm Caenorhabditis elegans is an effective model system for biological processes such as immunity, behavior, and metabolism. Robotic sample preparation together with automated microscopy and image analysis has recently enabled high-throughput screening experiments using C. elegans. So far, such experiments have been limited to per-image measurements due to the tendency of the worms to cluster, which prevents extracting features from individual animals. We present a novel approach for the extraction of individual C. elegans from clusters of worms in high-throughput microscopy images. The key ideas are the construction of a low-dimensional shape-descriptor space and the definition of a probability measure on it. Promising segmentation results are shown. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. UNSUPERVISED MACHINE LEARNING (USML) <s> Segmentation of tuberculosis bacilli in Zeihl-Neelsen tissue slide images is a crucial step in computer-assisted tuberculosis bacilli detection. In this paper, an automatic colour image segmentation using moving k-mean clustering was proposed. First, initial filter is used to remove the tissues images which remain blue after counterstaining process. After that, moving k-mean clustering using green component of RGB colour model and R_y component of C-Y colour model are used to segment the TB bacilli from its undesirable background which also remains red even after decolourization process. Then a 5×5 median filter and region growing was used to eliminate small regions and noises. The proposed methods have been analysed for several TB slide images under various conditions. Experimental results indicate that the proposed techniques were successfully segment TB bacilli from its background. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. UNSUPERVISED MACHINE LEARNING (USML) <s> Conventional clinical diagnosis of tuberculosis disease such as manual screening by microbiologist are tedious, laborious and time consuming. Therefore, more research has been carried out to develop technologies that able to automate the detection process. This paper presents an automated approach to tuberculosis bacilli detection in tissue section. The proposed approach employs image processing technique and neural network for the segmentation and detection of tuberculosis bacilli. First, images of tuberculosis bacilli in tissue samples are captured using light microscope after stained with Ziehl-Neelsen staining method. Then colour image segmentation using moving k-mean clustering is used to extract tuberculosis bacilli from the tissue image. Two colour spaces, RGB and C-Y colour, were utilised in order to improve the quality of segmentation and robust against various staining condition. Next, geometrical features of Zernike moments are calculated. From these features, the best features that could detect tuberculosis bacilli with higher accuracy were selected using hybrid multilayered perceptron network. Experimental results demonstrate that the proposed method is efficient and accurate to detect the tubercle bacilli in tissue. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. UNSUPERVISED MACHINE LEARNING (USML) <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> BIB006
In BIB001 a semi-automatic segmentation scheme for bacteria which takes into account each color separately is presented, where first RGB color separation is performed, then each color channel is converted to gray image and finally each color channel is segmented separately using adaptive neighborhood similarity comparison (spatial distance between each image pixel and its nearest point). In the experiments, 16 RGB images containing different kind of bacteria are used. Average accuracy of 96.70% is achieved in finding the foreground objects of interest from all channels separately. A self organizing tree map (SOTM) network is used to segment biofilms images so as to get insight of the internal parameters of the microbial in BIB002 . A set of features of 2D sample images are defined. These features are gray level (GL) of individual pixels, average gray level of a disk shaped region (GLdsk), phase congruency (PC) and position (XY) coordinates. The experiment is done in three different phases by changing criteria, first the network is allowed to grow to its maximum number of neurons up to 64, and by adding features one by one. Secondly, only XY features are varied where others are kept constant. Lastly dynamic features changes are allowed. The results show that few neurons are detected to represent the background and generally the results suggest that the SOTM can adaptively warp its topology into forms that reflect the type of segmentations typical to the human visual system. Fig. 19 shows the segmentation results by varying XY features. In BIB003 , a segmentation system to identify individual C. elgans from clusters of worms is presented, where segmentation of clusters from image is done first, by local adaptive thresholding and morphological opening followed by watershed segmentation and extensive merging. Then probabilistic model which is based on single worm to cluster area ratio and predefined path model, is applied to identify worms from clusters. In the experiments, 56 images each containing approximately 15 worms are used. The segmentation results are shown in Fig. 20 . In BIB004 , segmentation approach is proposed to segment TB bacilli from stained tissue images, where C-Y color model is used because of better mapping for luminance. Thus, Green and Ry channels of RGB and C-Y models respectively, are applied to moving k-mean clustering for initial segmentation (since bacilli are most highlighted and can clearly be seen in the green component of RGB and Ry component of C-Y color model), later a mean filter followed by region growing method are applied for fine segmentation. In the experiments, 30 to 50 RGB images from 25 tissue slides of TB positive samples are used and all images are further, categorized as normal, under-stained and over-stained images depending on the staining. The results of moving k-mean clustering above are used in BIB005 where, after segmentation median filter is used for denoising before Zemike moments features to be extracted, then classification takes place using hybrid multilayered perception network for detection of the bacilli. Finally the detection accuracy, sensitivity and specificity of 97.58%, 100% and 95.24% are obtained, respectively. Plasmodium vivax segmentation system is proposed in BIB006 , where the RGB to CMYk color conversion is performed and from this only C channel is selected as it is found that CMYk can give better presentation of the parasite in blood smear, then filtering is performed which is followed by segmentation using modified fuzzy divergence method which is based on Cauchy membership function, and capable of thresholding images with no distinct histogram valleys between background and foreground. Finally RGB color reconstruction is done to get the segmented RGB image. In the experiments, 150 RGB image frames of size 2048 × 1536 pixels captured from stained blood smears containing 