A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and the video acquisition system is described which explains how the second derivative best approximates the position of the edge. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Abstract A procedure using simple image processing and pattern recognition techniques to automatically extract and count organisms using images of bacterial cultures is described in this paper. Methanospirillum hungatei and Methanosarcina mazei were used for the experiments. The methanogens were grown in anaerobic serum vials for 1–3 week period. Samples of the growing cultures were withdrawn from the vials and photographed. The procedure was applied to images containing one kind of organism as well as images containing both type of organisms. The procedure separated and correctly identified the cultures as M. hungatei and M. mazei even in images where the organisms were overlapping or touching. The edge detection, the splitting, and the classification stages, by using this procedure, were accomplished in less than a minute. The image analysis and pattern recognition techniques described in this paper are simple, fast, and able to identify organisms based on their shapes. Our results indicate that such systems can be used to semi-automate routine culture-automate routine culture methods allowing rapid direct microscopic techniques for the detection, enumeration, and identification of bacteria. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Tuberculosis is a serious illness which control is mainly based on presumptive diagnosis. A technique commonly used consists of analyzing sputum images for detecting bacilli. However, the analysis of sputum is quite expensive, time consuming and requires highly trained personnel to avoid high errors. Image processing techniques provide a good tool for improving the manual screening of samples. In this paper we present a new bacilli detection technique with the aim to attain a high specificity rate and therefore for reducing the time required to analyze such sputum samples. This technique is based on the neuristic acknowlege extracted from the bacilli shape contour. It uses also the color information for image segmentation and finally a classification tree is used to categorize if a sample is positive or negative. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> In this paper, we describe the bivariate jointly distributed region snake method in segmentation of microorganisms in Single Exposure On- Line (SEOL) holographic microscopy images. 3D images of the microorganisms are digitally reconstructed and numerically focused from any arbitrary depth from a single recorded digital hologram without mechanical scanning. Living organisms are non-rigid and they vary in shape and size. Moreover, they often do not exhibit clear edges in digitally reconstructed SEOL holographic images. Thus, conventional segmentation techniques based on the edge map may fail to segment these images. However, SEOL holographic microscopy provides both magnitude and phase information of the sample specimen, which could be helpful in the segmentation process. In this paper, we present a statistical framework based on the joint probability distribution of magnitude and phase information of SEOL holographic microscopy images and maximum likelihood estimation of image probability density function parameters. An optimization criterion is computed by maximizing the likelihood function of the target support hypothesis. In addition, a simple stochastic algorithm has been adapted for carrying out the optimization, while several boosting techniques have been employed to enhance its performance. Finally, the proposed method is applied for segmentation of biological microorganisms in SEOL holographic images and the experimental results are presented. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Bacteria outnumber all other known organisms by far so there is considerable interest in characterizing them in detail and in measuring their diversity, evolution, and dynamics. Here, we present a system capable of identifying rod-like bacteria (bacilli) correctly in high resolution phase contrast images. We use a probabilistic model together with several purpose-designed image features in order to split bacteria at the septum consistently. Our method commits less than 1% error on test images. Our method should also be applicable to study dense 2D systems composed of elongated elements, such as some viruses, molecules, parasites (plasmodium, euglena), diatoms, and crystals. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple subregions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The objective of the present study is to develop an automatic tool to identify and classify the different types of spiral bacterial cells in digital microscopic cell images using active contour method. Geometric features are used to identify the arrangement of spiral bacterial cells, namely, vibrio, spirillum and spirochete. The current methods rely on the subjective reading of profiles by a human expert based on the various manual staining methods. In this paper, we propose a method for bacterial classification by segmenting digital spiral bacterial cell images and extracting only three geometric features for cell classification using different classifiers, namely, 3 classifier, K-NN classifier, Neural Network classifier and Neuro Fuzzy classifiers. The experimental results are compared with the manual results obtained by the microbiology expert and demonstrate the efficacy of the proposed method. <s> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Zooplankton is an important component in the water ecosystem and food chain. To understand the influence of zooplankton on the ecosystem a data collection is necessary. In research the automatic image based recognition of zooplankton is of growing interest. Several systems have been developed for zooplankton recognition on low resolution images. For large images approaches are seldom. Images of this size easily exceed the main memory of standard computers. Our novel automatic segmentation approach is able to handle these large images. We developed a contour based Split & Merge approach for segmentation and, to reduce the non-zooplankton segments, combine it with a pre-classification of the segments in reference to their shape. The latter includes a detection of quasi round segments and a novel one for thin segments. Experimental results on several large images show that we are able to handle them satisfactorily. <s> BIB007 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Abstract In this paper we present a new method for automated recognition of 12 microalgae that are most commonly found in water resources of Thailand. In order to handle some difficulties encountered in our problem such as unclear algae boundary and noisy background, we proposed a new method for segmenting algae bodies from an image background and proposed a new method for computing texture descriptors from a blurry texture object. Feature combination approach is applied to handle a variation of algae shapes of the same genus. Sequential Minimal Optimization (SMO) is used as a classifier. An experimental result of 97.22% classification accuracy demonstrates an effectiveness of our proposed method. <s> BIB008 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Visceral Leishmaniasis is a parasitic disease that affects liver, spleen and bone marrow. According to World Health Organization report, definitive diagnosis is possible just by direct observation of the Leishman body in the microscopic image taken from bone marrow samples. We utilize morphological and CV level set method to segment Leishman bodies in digital color microscopic images captured from bone marrow samples. Linear contrast stretching method is used for image enhancement and morphological method is applied to determine the parasite regions and wipe up unwanted objects. Modified global and local CV level set methods are proposed for segmentation and a shape based stopping factor is used to hasten the algorithm. Manual segmentation is considered as ground truth to evaluate the proposed method. This method is tested on 28 samples and achieved 10.90% mean of segmentation error for global model and 9.76% for local model. <s> BIB009 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Abstract Environmental microorganisms (EMs) are single-celled or multi-cellular microscopic organisms living in the environments. They are crucial to nutrient recycling in ecosystems as they act as decomposers. Occurrence of certain EMs and their species are very informative indicators to evaluate environmental quality. However, the manual recognition of EMs in microbiological laboratories is very time-consuming and expensive. Therefore, in this article an automatic EM classification system based on content-based image analysis (CBIA) techniques is proposed. Our approach starts with image segmentation that determines the region of interest (EM shape). Then, the EM is described by four different shape descriptors, whereas the Internal Structure Histogram (ISH), a new and original shape feature extraction technique introduced in this paper, has turned out to possess the most discriminative properties in this application domain. Afterwards, for each descriptor a support vector machine (SVM) is constructed to distinguish different classes of EMs. At last, results of SVMs trained for all four feature spaces are fused in order to obtain the final classification result. Experimental results certify the effectiveness and practicability of our automatic EM classification system. <s> BIB010 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Environmental microorganisms (EMs) such as bacteria and protozoa are found in every imaginable environments. To explore functions of EMs is an important research field for environmental assessment and treatment. However, EMs are traditionally investigated through morphological analysis using microscopes or DNA analysis, which is time and money consuming. To overcome this, we introduce an innovative method which applies content-based image analysis (CBIA) to environmental microbiology. Our method classifies EMs into different categories based on features extracted from microscopic images. Specifically, it consists of three steps: The first is image segmentation which accurately extracts the region of an EM in a microscopic image with a small amount of user interaction. The second step is feature extraction where multiple features are extracted to describe different characteristics of the EM. In particular, we develop an internal structure histogram descriptor which captures the structure of the EM using angles defined on its contour. The last step is fusion which combines classification results by different features to improve the performance. Experimental results validate the effectiveness and practicability of our environmental microbiology method aided by CBIA. <s> BIB011 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Due to the very low contrast between the microorganisms and background in sewage microscopic images, few traditional segmentation algorithms are available to extract the object contours from surrounding environment completely and smoothly. In this paper, we propose a novel contour extraction method for microorganisms in sewage micro-images. Firstly, Sobel operator is utilized to calculate the image gradient values. Then, four adaptive thresholds are designed to extract the real object edges and protect the weak edges at the same time. Finally, complete and smooth contours of various microorganisms are obtained based on rational edge connection mechanisms which are proposed in detail. Experiment results show the effectiveness of our method compared with other edge detection algorithms. <s> BIB012 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Image processing and analysis is a useful tool for monitoring of activated sludge wastewater treatment plants. However, its effectiveness is dependent on performance of the segmentation algorithms. The activated sludge wastewater plant can be monitored by image processing and analysis of images acquired through microscope using bright field microscopy and phase contrast microscopy. In this paper, we have investigated three segmentation algorithms which are channel based segmentation, edge based segmentation and Bradley based segmentation. The performance of the algorithms is assessed using the performance metric of accuracy. Forty gold approximations of ground truth images are manually prepared for comparing with the result for segmentation. Half of the forty images are acquired at 10x and rest at 20x objective magnification of the microscope. Edge based segmentation gives better results compared to other algorithms with accuracy of 0.972. <s> BIB013 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms. <s> BIB014 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. EDGE BASED SEGMENTATION (EBS) <s> In our previous work on automated microalgae classification system we proposed the multi-resolution image segmentation that can handle well with unclear boundary of algae bodies and noisy background, since an image segmentation is the most important preprocessing step in object classification and recognition. The previously proposed approach was able to classify twelve genera of microalgae successfully; however, when we extended it to work with new genera of filamentous algae, new challenging problems were encountered. These difficulties arise due to a variety of the forms of filamentous algae, which complicates both image segmentation and classification processes, resulting in substantial degradation of classification accuracy. Thus, in this work we propose a modified version of our multi-resolution segmentation algorithm by combining them in such a way that the strengths of both algorithms complement each other’s weaknesses. We also propose a new skeleton-based shape descriptor to alleviate an ambiguity caused by multiple morphologies of filamentous forms of algae in classification process. Effectiveness of the two proposed approaches are evaluated on five genera of filamentous microalgae. SMO is used as a classifier. Experimental result of 91.30% classification accuracy demonstrates a significant improvement of our proposed approaches. <s> BIB015
In BIB002 , a segmentation and classification framework is introduced to identify individual microorganism from a group of overlapping (touching) bacteria. Canny edge segmentation is applied prior to Yanowitz bruckstein which are found to give better segmentation results on such images containing non uniform illuminating backgrounds, then shape coefficient and eigenvalue ratios are used as features for classifying between individual microorganisms and in which particular group they belong. In the experimental analysis, contrasts photomicrographs of growing culture were used as inputs images and two Methanogenic bacteria are used as model organisms (Methanosprillum hungatei and Methanonosarcina mazei). Two categories of images are used, one category is the image set containing only one specie of microorganism. Another is the one containing mixture of two species. Finally, the result is given in counts of bacteria in tested images. And it shows that the count is very near to accurate manual count. Fig. 13 shows the segmentation work flow. In BIB001 , evaluation of suitable segmentation (technique which is capable of segmenting bacteria and protista biomass so as to understand their population parameters) is presented. are used, cyanobacterua, cryptophyte, marine phytoplankton, flagellate and ciliates. Comparing all the tested approaches, the best one is minimum of the second derivative (MIND2) which achieves the average lowest segmentation errors. An automatic identification of tuberculosis bacteria (from fluorescent microscopic images of stained samples with fluorochrome auramine O) is proposed in BIB003 . Since bacteria appears brighter yellowish on tested images, Canny edge technique is applied for edge detection, followed by a non-maxima suppression and a hysteresis threshold operations. Because some structures can appear broken, a morphological closing operation is applied. Then compactness and eccentricity features are extracted in one branch, and the same segmented images are passed through k-means clustering in the second branch, then each branch independently goes to classification part using nearest neighbor classifier. A total of 397 negative and 75 positive RGB images are used. The average results obtained from two branches are, 93.30% and 100% sensitivity for the first and the second branch, respectively. To be able to segment 3D holograhic images of microorganism, an approach is proposed in BIB004 , which uses statistical region snake (active contour model) method which is performed in many number of iterations to have better segmentation results. 3D digitally constructed images of sphacelaria algae and diatom algae are used for experiment. The obtained result is shown in Fig. 14 . In BIB005 , a system capable of identifying individual rod like bacteria (bacilli) in population is presented, where segmentation is performed using Marr Hildreth edge detector, then detection of beaded structure which characterizes the shape of individual bacteria is used for identification from cluster using probabilistic models. The algorithm is evaluated using, An approach based on active contour segmentation of about 1500 iterations is used in identification of rotavirus-A in  . After the segmentation of gray images, shape features like area, eccentricity, perimeter, circularity, length per width ratio and compactness are extracted and then identification is done by examining features if they are within max-min required ranges. 50 RGB digital images (resolution of 12-16 bits) of rotavirus-A particles (non-overlapping) taken from transmission electron microscopy are used for experiment. Finally this method yields an identification rate (accuracy) of 98.00%. The same method is applied in BIB006 , for the analysis of spiral bacteria cell images, however, the classification is done using fuzzy classifier which yields an accuracy of 100%. During experiments, 1280 images containing three species of spiral bacteria are used (vibro spiral, spirochete and sprillium). A method for segmentation and classification of large size images of zooplanktons is proposed in BIB007 . A split and merge active contour based method is used for segmentation. Firstly, images are split into tiles, then Otsu's is applied on each tile, after a morphological closing, a hough transform for circle detection is used to determine the starting contour points. Then active contour algorithm is executed on the binary image, finally, the tiles are merged before classification. In testing the algorithm, RGB images of zooplanktons of size 60000 × 60000 pixels are used. The segmentation approach is compared with longest contour and region growing approaches and the results show that the proposed active contour approach achieves the highest recall of 99.70%. In BIB009 , active contour based model (Chan-Vese (CV) model) is used in segmentation of Leishmania parasite in microscopic images, which is done after linear contrast and morphological operations. Segmentation is done in two ways, in the first approach termed as local method, images are split into small patches (masks) then segmentation is done on these patches while the other approach uses non split images and this way is termed as global method. During experiment, 20 RGB microscopic images of resolution 3246 × 2448 pixels containing Leishman parasites taken from bone marrow samples are used. The proposed method is compared with other Otsu, Sauvola and k-means segmentation methods. Better performance is achieved by the proposed method by an accuracy of 89.10% and 90.24%, for global and local approaches, respectively. In BIB010 , a semi-automatic segmentation is used in content based analysis and classification of environmental microorganisms, in this approach segmentation is done using Sobel edge detector, followed by feature extraction in which four features are extracted (histogram descriptor, geometric feature, Fourier descriptor and internal structure histogram (ISH), then classification of each environmental microorganism is done using SVM, finally late fusion of outcomes of SVMs for each tested class of microorganism is done. In the experiments, 10 classes of environmental microoganisms (such as Dicrmiphorus, Paramecinium and verticella) are used with each class represented by 20 microscopic images. The proposed segmentation method achieves an accuracy of 98.70%. To increase the accuracy of the classification results, in BIB011 local features (SIFT) are additionally extracted and using the same segmentation and classification techniques, 20 images from each 15 classes of environmental microorganisms are used. This approach achieves the classification results of 97.70% accuracy. Furthermore, similar segmentation approach is applied in  -  . In BIB012 , to tackle the hardship of segmentation on distorted images due to motion in waste water treatment systems, a Sobel edge detection method with multiple gradient thresholds is adopted to obtain better target edges, the proposed method is compared with conventional Sobel, Log and Canny edge based segmentation which are all done after image denoising. RGB microscopic images of microbacteria from waste water samples are used for experiment. The segmentation results produced by the proposed method and other compared methods are shown in Fig. 15 . Three segmentation methods of floc and filament in activated sludge waste water treatment plants are applied and evaluated in BIB013 . These methods are edge based segmentation (using Sobel), channel based segmentation using HSV format (global threshold is applied for segmentation), and Bradley thresholding method (in which local adaptive technique is used to generate binary image by thresholding local mean evaluated over a specific window size). RGB images of size 1224×960 pixels from activated sludge waste water samples are used during experiments. The segmentation results show that the edge based segmentation outperforms the other methods with an accuracy of 97.16%. In BIB014 , nine methods for segmentation of floc from phase contrast microscopic images of activated sludge (during monitoring of waste water treatment) are assessed. These methods are saturation color channel based approach, edge detection approach (Sobel based), k-means clustering algorithm, adaptive thresholding, texture based segmentation (Bradley adaptive thresholding), watershed algorithm, Kittler thresholding, split-merge approach and top-bottom-hat filtering based method. These methods are evaluated using, 61 RGB (phase contrast microscopic) images of size 1224×960 pixels captured from activated sludge. The evaluation results of the best four methods are considered. Among them edge based method outperforms, with the highest accuracy of 99.70%, followed by watershed method with an accuracy of 99.67%. To be able to segment filamentous algae in low contrast and noisy images in BIB015 , segmentation results of two pipelines which focus on edge and region respectively, are combined using iterative erosion technique ( region based ouput image is eroded several times to fit into edge based output segmentation results). The edge based approach involves, first, extraction of only blue channel (which shows distinct edges) from RGB, followed by adaptive thresholding on blue channel, then small unwanted objects are removed by morphological opening operation. The second approach which is proposed in BIB008 , involves, Sobel edge detection, followed by canny which uses large smoothing parameter for roughly estimating the boundary of the elgae in image, then fine segmentation is achieved by applying Gaussian low pass filter of size 20 × 20 pixels and sigma of 0.5 in order to suppress the background and remain with foreground. In the experiments, 300 RGB images containing one among five species of filamentous algae (Anabaena, Oscillatoria, Spirogyra, Spirulina, Anabaenopsis) are used. The final segmentation results obtained are near to manual results.