A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. REGION BASED SEGMENTATION (RBS) <s> We explain a system for automatic cell segmentation that uses Matlab and the free DIPImage package... <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. REGION BASED SEGMENTATION (RBS) <s> In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. REGION BASED SEGMENTATION (RBS) <s> Medical image analysis is very challenging due to idiosyncrasies of medical profession. Object recognition with data mining techniques has helped doctors in case of medical emergencies for the image analysis, pattern identification and treatment. Over 180 million people died and more than one third of the population is carrier of Mycobacterium Tuberculosis (TB) bacteria as per the WHO statistics [1-5]. Segmentation of TB from the stained background is very challenging due to noise and debris in the image. In this paper, an automated segmentation of tuberculosis bacterium using image processing techniques is presented. Colour segmentation with region growing watershed algorithm is proposed for the bacterial identification. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. REGION BASED SEGMENTATION (RBS) <s> In order to avoid the over-segmentation problem caused by original watershed transform and improve the segmentation precision of Mycobacterium Tuberculosis (MTB) images, a novel segmentation algorithm is proposed based on automatic-marker watershed transform. The automatic marker is accomplished by Gaussian weighted adaptive threshold segmentation and local minimum points search within gradient image. After the stage of initial segmentation based on watershed, regions are merged with the criterion of max similarity of adjacent regions in order to obtain integrated objects. Finally, the multi-thresholds segmentation for eliminating contaminations is employed to improve the robustness of the algorithm. Experimental results demonstrated that the proposed algorithm can achieve superior segmentation accuracy in the images with different background colors. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. REGION BASED SEGMENTATION (RBS) <s> With widening interests in using model organisms for reverse genetic approaches and biomimmetic micro-robotics, motility phenotyping of the nematode Caenorhabditis elegans is expanding across a growing array of locomotive environments. One ongoing bottleneck lies in providing users with automatic ne- matode segmentations of C. elegans in image sequences featuring complex and dynamic visual cues, a first and necessary step prior to extracting motility phenotypes. Here, we propose to tackle such automatic segmentation challenges by introducing a novel Texture Feature Model (TFM). Our approach revolves around the use of combined intensity- and texture-based features integrated within a probabilistic framework. This strategy first provides a coarse nematode segmentation from which a Markov Random Field (MRF) model is used to refine the segmentation by inferring pixels belonging to the nematode using an approximate inference technique. Finally, informative priors can then be estimated and integrated in our framework to provide coherent segmentations across image sequences. We validate our TFM method across a wide range of motility environments. Not only does TFM assure comparative performances to existing segmentation methods on traditional environments featuring static backgrounds, it importantly provides state-of-the-art C. elegans segmentations for dynamic environments such as the recently introduced wet granular media. We show how such segmentations may be used to compute nematode “skeletons” from which motility phenotypes can then be extracted. Overall, our TFM method provides users with a tangible solution to tackle the growing needs of C. elegans segmentation in challenging motility environments. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. REGION BASED SEGMENTATION (RBS) <s> Purification of waste water is commonly done using the activated sludge process. The ratio of the activated sludge flocs and filamentous bacteria play a key role in the purification process of waste water. The sludge bulking or filamentous bulking is a common problem in activated sludge plants that prevents flocs to settle down. Digital imaging techniques can play an important role in monitoring activated sludge flocs and filaments in waste water treatment plants (WWTPs). In this paper, an algorithm to segment the flocs and the filaments of the microscopic sludge images captured at 4 times magnification in brightfield microscopy has been proposed. Morphological parameters, like, compactness, roundness, convexity, equivalent diameter are analyzed. Comparison with laser particle size analysis method has been done for the interpretation of the imaging results. <s> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. REGION BASED SEGMENTATION (RBS) <s> Abstract The sputum smear microscopy based tuberculosis (TB) screening method is a conventional method employed for disease identification. It provides significant benefit to TB burdened communities across the globe; however, there are many challenges faced in processing the sputum smear images. When the smear is thick or uneven the number of overlapping bacilli is more which impedes the diagnosis. The separation of overlapping bacilli is significant without which the results lead to gross errors in identification of the disease causing agent. In this work, separation of overlapping bacilli is carried out by method of concavity (MOC) and is compared with the conventional methods such as multi-phase active contour (MAC) and marker-controlled watershed (MCW). Performance of the methods is evaluated based on the statistical mean quality score of shape descriptors extracted from the separated and existing true bacilli. The shape descriptors employed in this work include geometric features, Hu's, Zernike moments and Fourier descriptors. Results of separated overlapping bacilli demonstrate that MOC performs better than MAC and MCW. It is observed that the statistical mean quality score of the separated bacilli using the proposed MOC shows nearest match with true bacilli. The validation performed with experimental results to that of human annotations highlights the performance of MOC in separating the overlapping bacilli in the sputum smear images. <s> BIB007 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. REGION BASED SEGMENTATION (RBS) <s> Sputum smear conventional microscopy (CM) is used as primary bacteriological test for detection of TB. This technique is the most preferred technique in low and middle income countries due to its availability as well as accessibility. Manual screening of bacilli using CM is time consuming and labor intensive. As a result, the sensitivity of TB detection is compromised leading to misdiagnosis 33–50% of active cases. Automated methods can increase the sensitivity and specificity of TB detection. Currently, the remote areas of TB-endemic developing countries have easy accessibility to portable and camera-enabled Smartphone microscope for capturing images from ZN-stained smear slide. In this paper, the performance of watershed segmentation method for detection and classification of bacilli from camera-enable Smartphone microscopic images is presented. Several preprocessing techniques have been implemented prior to watershed segmentation. Current method has achieved the sensitivity and specificity of 93.3% and 87% respectively for classifying an image as TB positive or negative. <s> BIB008
In BIB001 an automatic segmentation system of bacteria microscopic images is developed, this system first segments the bacteria clusters from background using Otsu followed by region growing methods, secondly, seeds are selected at the center of individual bacteria in clusters, then seed watershed is applied to segment individual bacteria from clusters. This differs from the original watershed algorithm in that water is only pumped in at each seed location rather than at every local minima. RGB microscopic images containing B. subtilis bacteria are used during experiments. In  , segmentation and classification approach for rotavirus-A is proposed. Segmentation is performed on gray level images using marker controlled watershed method, which is able to identify closely spaced particles as individual objects in tested images. Then extracted shape features are used to train and test 3α classifier and min-max classifier. In this experiments, 50 RGB digital images containing rotavirus-A particles (non-overlapping) taken from transmission electron microscopy are used. Finally, 3α classifier method yields better results 98.00% classification rate. A segmentation method for enhancing visualization and analysis of 3D laser scanning microscopic images of C. elegan cells is introduced in BIB002 . The approach involves five steps which are, acquisition, registration, segmentation using adaptive 3D watershed algorithm, reconstruction and analysis (quantification) of individual C. elegan cells based on their relative location patterns in the 3D standardized space. 3D input images of C. elegan taken from fluorescent laser scanning microscope are used for testing the algorithm. The final results are as shown in Fig. 10 . A contour based seed region growing watershed technique is used to segment TB bacteria from their background in BIB003 . Before segmentation, color conversion to Lab color mode and denoising using image filling holes operations are applied. Then Hu moment and geometric features are used to further isolate the bacteria. During experiments, 516 RGB images with size 2080 × 1542 pixels are used for segmentation. In BIB004 , based on illumination conditions, different background colors, weak edge and low contrast which hinder better results in identification of bacteria in microscopic images of ZN stained sputum smear, an approach is proposed. Firstly, the Gaussian weighted adaptive threshold segmentation and local minimum points search within gradient image are applied to obtain the required markers. Then the marker-based watershed transform is employed for initial segmentation. Finally, multi-thresholding is applied for fine segmentation. In the experiment, 50 RGB images of positive TB are used. A segmentation accuracy of 83.8% is achieved. For segmentation and motility modeling of C. elegans in BIB005 , a single annotated image is used to compute a number of image texture features within a Bayesian model to infer which pixels belong to the nematode and which ones do not, then the mean-field variational inference technique is applied to compute the maximum posterior marginals of the pixel labels. Finally, Bayesian filtering, provides coherent estimates to where the nematode is located across the image sequence. 35 images each from different motility environments for C. elegans are used (crawling on an agar surface, swimming in an aqueous drop, swimming in gelatin based solutions, navigating through mazes constructed of micropillars, movement of nematode in aqueous buffer solution containing monodisperse particles and movement of nematode in aqueous buffer solution containing polydisperse particles). The proposed approach shows improved results compared to previous traditional intensity based thresholding and multi-environment model estimation (MEME) algorithms. In BIB006 , a system to monitor morphological features of floc and filaments in activated sludge which uses segmentation approach is introduced. It uses H-minima transform (region based method), followed by median filter, finally conventional binary conversion and area opening operation are applied. 30 grey scale images captured under the bright field microscope from activated sludge samples are used in the experiments. Fig. 11 shows the flow of image from original to segmented one. In order to separate overlapping bacilli a method of concavity (MOC) is used in BIB007 , where concavities are found using the convex hull of the segmented binary image. Fig. 12 shows the MOC algorithm. 100 images of size 4164 × 3120 pixels containing overlapping bacilli are used during experiments. Finally, the individual counts of TB bacteria is obtained after segmentation. MOC results are compared with manual results, and other conventional methods (multi-phase active contour and marker controlled watershed). The results of MOC are more similar to manual (ground truth). In BIB008 , a detection algorithm is proposed to segment and classify TB bacilli from microscopic images captured by smart phone camera. In the algorithm, RGB to gray level conversion takes place, followed by contrast enhancement, then thresholding and morphological operations are applied, after this, the artefacts are removed based on shape and size (to remain with bacilli only), then watershed method is applied for separation of overlapping (touching) bacilli, finally counting and labelling of bacilli is done. In evaluation of the proposed method, 30 RBG images of ZN stained bacilli taken from smart phone camera enabled microscope are used. 15 images are TB positive and 15 are TB negative. The overall segmentation result of the proposed method is 87.00% specificity.