A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Abstract The problem of automatic threshold selection is considered. After a brief review of available techniques, a novel method is proposed. It is based on image statistics which can be computed without histogramming the grey level values of the image. A detailed analysis of the properties of the algorithm is then carried out. The effectiveness of the method is shown on a number of practical examples. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> The determination of volumes and interface areas from confocal laser scanning microscopy (CLSM) images requires the identification of component objects by segmentation. An automated method for the determination of segmentation thresholds for CLSM imaging of biofilms was developed. The procedure, named objective threshold selection (OTS), is a three-dimensional development of the approach introduced by the popular robust automatic threshold selection (RATS) method. OTS is based on the statistical properties of local gray-values and gradients in the image. By characterizing the dependence between a volumetric feature and the intensity threshold used for image segmentation, the former can be determined with an arbitrary confidence level, with no need for user intervention. The identification of an objective segmentation procedure renders the possibility for the full automation of volume and interfacial area measurement. Images from two distinct biofilm systems, acquired using different experimental techniques and instrumental setups were segmented by OTS to determine biofilm volume and interfacial area. The reliability of measurements for each case was analyzed to identify optimal procedure for image acquisition. The automated OTS method was shown to reproduce values obtained manually by an experienced operator. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> One serious problem among the troubles to identify objects in an optical microscopic image is contour background due to non-uniform light source and various transparency of samples. To solve this problem, this paper proposed an elimination method of the contour background and compensation technique as follows. First, Otsu's optimal thresholding method extracts pixels representing background. Second, bilinear interpolation finds non-deterministic background pixels among the sampled pixels. Third, the 2D cubic fitting method composes surface function from pivoted background pixels. Fourth, reconstruction procedure makes a contour image from the surface function. Finally, elimination procedure subtracts the approximated background from the original image. To prove the effectiveness of the proposed algorithm, this algorithm is applied to the yeast Zygosaccharomyces rouxii and ammonia-oxidizing bacteria Acinetobacter sp. Labeling by this proposed method can remove some noise and is more exact than labeling by only Otsu's method. Futhermore, we show that it is more effective for the reduction of noise. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Bacteria segmentation of particular species entails a challenging process. Bacteria shape is not enough as a discriminant feature, because there are many species that share the same shape. We present here two methods for tuberculosis image segmentation using the chromatic information. The first method is based on fuzzy segmentation of the color images based on the information that it is entailed in each separate chromatic histogram. The second method is a simple color filtering account by comparison of the inverse of the yellowish stained bacteria (blue channel) with the product of the other two chromatic channels. The third method is based on the extraction of image signatures by projecting logarithmic-polar mappings onto 1D vectors. This representation provides a very compact description of all image aspects, in cluding shape, texture and color. An achromatic segmentation method is also presented based on the use of gray-level morphological operators only to the green channel. Finally we present the results of different autofocusing algorithms of stained tuberculosis images. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Most of the thresholding procedures involved setting of boundaries based on grey values or intensities of image pixels. In this paper, the thresholding is to be done based on color values in images of Ziehl-Neelsen sputum slides. The color thresholding technique is being carried out based on the adaptation and slight modification of the grey level thresholding algorithm. Multilevel thresholding has been conducted to the RGB color information of the bacterium to extract it from the sputum and other objects. Five types of different images have been used in the study of color information. The results showed that by using the selected threshold values, the image segmentation technique has been able to separate the sputum from the mycobacterium. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Nowadays an increasing research interest in the field of biotechnology has been drawn to achieve reliable information from model organisms. C. Elegans nematode worm is one of the major animals. Machine vision analysis of this animal needs to solve many important problems, e.g. detection of each individual in population images, movement patterns of isolated and overlapped worms and so on. In this paper, we describe our recently proposed method with an analysis to show the impact of two major parameters on the method efficiency. Based on our analysis on 255 isolated and overlapped worms, we find that our method prepares the best correct detection (TAR=83%) for thetasTh and NTh between 15 and 20 pixels. <s> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Quality of tuberculosis (TB) diagnosis by manual observation varies depending on the quality of the smear and skill of the pathologist. To overcome this problem, a method for diagnosis of TB from ZN-stained sputum smear images is presented in this paper. Hue color component based approach is proposed to segment the bacilli by adaptive choice of the hue range. The bacilli are declared to be valid or invalid depending on the presence of beaded structure inside them. The beaded structure is segmented by thresholding the saturation component of the bacilli pixels. Clumps of bacilli and other artifacts are removed by thresholding the area, thread length and thread width parameters of the bacilli. Results presented for several images taken from different patients show that the scheme detects the presence of TB accurately. <s> BIB007 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> A microbial biofilm is structured mainly by a protective sticky matrix of extracellular polymeric substances. The appreciation of such structures is useful for the microbiologist and can be subjective to the observer. Thus, quantifying the underlying images in useful parameters by means of an objective image segmentation process helps substantially to reduce errors in quantification. This paper proposes an approach to segmentation of biofilm images using optimal multilevel thresholding and indices of clustering validity. A comparison of automatically segmented images with manual segmentation is done through different thresholding criteria, and clustering validity indices are used to find the correct number of thresholds, obtaining results similar to the segmentation done by an expert. <s> BIB008 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> The appreciation of biofilm structures in digital images can be subjective to the observer, and hence it is necessary to analyse the underlying images in useful parameters by means of quantification that is, ideally, free of errors. This paper proposes a combination of techniques for segmentation of biofilm images through an optimal multi-level thresholding algorithm and a set of clustering validity indices, including the determination of the best number of thresholds. The results, which are validated through Rand Index and a quantification process performed in a laboratory, are similar to the quantification and segmentation done by an expert. <s> BIB009 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> A major challenge in microbial ecology is to develop reliable and facile methods of computer assisted microscopy that can analyze digital images of complex microbial communities at single cell resolution, and compute useful quantitative characteristics of their organization and structure without cultivation. The objective of the present study is to develop an automatic tool to identify and classify the bacterial cells in digital microscopic cell images. Geometric features are used to identify the different types of spiral bacterial cells, namely, vibrio, spirillium 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 bacterial cell images and extracting geometric features for cell classification using 3σ classifier, K-NN classifier, Neural Network classifiers and 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> BIB010 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a ::: process often used as disease progression readout and to develop therapeutic strategies. This work presents an image ::: processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode ::: Caenorhabditis Elegans. ::: A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals ::: of distinct genetic conditions. Because of the animals' transparency, most of the slices pixels appeared dark, hampering ::: their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in ::: order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm ::: that uses the Tukey's biweight as edge-stopping function. The image histogram median of this outcome was used to ::: dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm ::: and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial ::: animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations ::: were subsequently segmented based on an iso-value and blended with the resulting volume mesh. ::: The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and ::: high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative ::: diseases treatment planning and interventions prevention. <s> BIB011 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Monitoring water quality using biological monitoring technology based on microscopic examination of activated sludge, machine vision replaces manual observation. The contrast between target and background is relatively low in the images obtained by automatic electron microscope. It is difficult to accurately distinguish the target and background if single image segmentation method is used. Therefore, the image which is segmented by single image segmentation method will be regarded as the source image, and image fusion is performed on these source images using wavelet transform. The fused image will be regarded as the final segmentation image. The result of experiments shows that using this idea can get better image edge detection effect, and it will provide good technical basis for the next protozoan and metazoan identification and statistics. <s> BIB012 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Image segmentation is important in analysis of medical images. However, there are many constraints that must be faced such as variety of biological system and also limitation of mechine equipment. Thus, effective and precise method are needed to obtain correct diagnosis of quantitative medical samples. This paper studies the comparison between color thresholding and global thresholding techniques for segmenting Ziehl-Neelsen TB bacilli slide images in sputum samples. Color thresholding use RGB image as input while global thresholding used C-Y image as input. This technique has been tested onto 50 different images. The results obtained indicate that global thresholding method have better potential to ease next stage of image processing. <s> BIB013 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region- and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics. <s> BIB014 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> We present a method for segmenting 2D microscopy images of freshwater green microalgae. Our approach is based on a specialized level set method, leading to efficient and highly accurate algae segmentation. The level set formulation of our problem allows us to generate an algae's boundary curve as the result of an evolving level curve, based on computed background and algae regions in a given image. By characterizing the distributions of image intensity values in local regions, we are able to automatically classify image regions into background and algae regions. We present results obtained with our method. These results are very promising as they document that we can achieve highly accurate algae segmentations when comparing ours against manually segmented images (segmented by an expert biologist) and with results derived by other approaches covered in the literature. <s> BIB015 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Image processing and analysis is a potential alternative monitoring tool for biodegradation in activated sludge wastewater treatment process. Accuracy of image analysis based predictive models depends on the quality of the segmentation of the microbial aggregates in microscopic images of activated sludge. The segmentation of the images is hindered by irregular illumination and properties of the microbial aggregates such as varying opaqueness and size. In this paper, an iterative region based Otsu thresholding is proposed for the bright field microscopic images of activated sludge. The suggested approach takes not only the statistics of grayscale intensities into account but also the regional distribution of the illumination noise. The proposed algorithm is compared with state-of-the-art Otsu, iterative Otsu and local Otsu segmentation techniques. The performance of the algorithms is assessed using accuracy, Rand index (RI) and variation of information (VI). The proposed algorithm exhibited better performance in terms of all the metrics with accuracy 0.9854, RI 0.9721 and VI 0.2141. <s> BIB016 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Malaria is a disease caused by parasitice protozoa belonging of the genus Plasmodium through the mediation of prick (bite) of Anopheles. Indonesia is one of the countries that has a high endemicity. There are four types of plasmodium that can cause malaria, which are plasmodium falciparum, plasmodium vivax, plasmodium oval and plasmodium malariae. One of the common ways to detect malaria parasites is to use microscopic image. Detection using microscopic imaging has made a significant contribution in the diagnosis of malaria infection by blood imagery. This study proposes a color image segmentation of the malaria parasite, In order to accelerate the diagnostic process and to achieves high detection accuracy malaria. <s> BIB017 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. THRESHOLD BASED SEGMENTATION (TBS) <s> Protozoa identification plays an important role in parasitology in order to give right analyses and treatments. Previous identification methods analyze protozoa using morphology on microscopic images. In this work, we focus on a set of specific species which cause disease to human such asGiardia lambia, Iodamoeba butchilii, Toxoplasma gondi, Cyclospora cayetanensis, Balantidium coli, Sarcocystis, Cystoisospora belli and Acanthamoeba. They share the similar rounded shapes that make it difficult to identify them. We propose 3D Geometric Multiple Color Channel Local Feature to characterize these species of protozoa. Our method groups key points of the local feature with respect to 2D geometric in planar images and scale-level from multiple color channels of input images to produce feature vectors for identification task. Support Vector Machine is used as the classifier to identify the species of protozoa. Experiments on our dataset show the potential of our proposed method with a good performance of 96% of accuracy and outperformed previous methods. <s> BIB018
For accurate segmentation of biofilm clusters which are characterized by unclear marking boundaries,  compares entropy and histogram based techniques, which are; Local entropy which uses spatial correlation in the image as criteria for selecting optimal threshold, joint entropy, relative entropy, Renyi's entropy, and iterative entropy (which is based on histogram and assumes the optimal threshold by averaging the mean value for both background and foreground pixels thus, it is suitable for poor contrast images). In the experiment, 660 × 480 pixel size, 8 bit (256 gray level) images of biofilms are used. With reference to manually segmented images as ground truth, the iterative selection method outperforms other methods by regression coefficient of 96.00% Since segmentation is required prior to volume and interface area measurement of biofilm, in 3D image analysis from confocal laser scanning microscope (CLSM) BIB002 , an objective threshold selection (OTS) technique, for selection of optimal threshold value is proposed in BIB002 . The optimal threshold value is found by finding the median of individual values on perpendicular plane of the 3D biofilm (derived from robust automatic threshold selection (RATS) method BIB001 ). During experiments, biofilms from two kinds of culturing media are used (denitrifying and phenanthrene) and the segmentation results achieved are near to manual results. A related work is presented in  , where, OTS approach is compared with 3D otsu based method on segmentation of CLSM biofilms images of P.mirabilis and S.aureus. In Otsu based approach, images of pixel size 1024 × 1024 are passed through anisotropic diffusion filter before performing 3D otsu segmentation. The results show that, Otsu based method in combination with filtering, gives better results than OTS. Fig. 4 shows the segmentation results of both methods on same dataset as presented in  . In segmentation of TB bacteria which are characterized by yellowish color in the fluorochrome auramine O stained sputum, color based segmentation which provides a broader discrimination between object borders is presented in BIB004 . The best channel for segmentation in RGB (which gives the optimal threshold value), is found to be blue by using Yager function for fuzziness. Then images are thresholded based on the value obtained in histogram of blue channel. Dilation and closing morphological operations are applied on segmented images, before shape features being extracted for classification. In this approach a set of 80 images from positive TB bacilli and 50 negative images are used for training and testing the classifier. This method achieves segmentation results with a specificity of 82.00%. A segmentation technique for identification of Mycobacterium tuberculosis low contrast conventional light microscopic images of sputum is proposed in  , where, subtracting green channel from red (R-G) is found to give a distinct visual identification of TB bacteria from the background, then binarization is performed using global adaptive thresholding for final segmentation. The algorithm is evalutated using 50 RGB images of size 3072 × 2304 pixels, obtained from positive TB sputum samples. Quite satisfactory segmentation results of 75.65% sensitivity are obtained by the proposed technique. In BIB005 , a color based approach for segmentation of TB bacteria from ZN stain sputum sample images is presented, where the RGB images are first divided into their corresponding R, G, B color channels, then threshold based segmentation is applied on each channel image separately. To get the segmented image the output of all channels are combined using AND operation (The pixel is defined as part of the foreground if its three RGB components all lie within the selected range). In the experiments, five images (of different types) are considered for initial studies and other 50 images for testing. The segmentation results are indicated in Fig. 5 . This approach is compared with global thresholding method on the same dataset in BIB013 , where, global thresholding is performed based on the C-Y color information of the image pixels which contains TB. The technique retains the red pixels of TB bacilli which fall in the range of hue 0 0 till 120 0 while the other pixels which fall in the range of 121 0 till 360 0 are eliminated. The results show that global thresholding produces better results compared to previous color thresholding, as it is able to reduce more noise and sputum background from the images. Fig. 6 shows the flow chart for comparison. In BIB006 , an edge linking based method to detect and separate individual C. elegans worms in culture is proposed. Gaussian smoothing with 20 × 20 pixels mask is applied, then subtraction of blurred image from original image is performed to keep the high frequency pixels (the edges). Then global thresholding is performed on the high frequency image. Finally, morphological skeletonization is performed followed by closing operations (to isolate and remove pixels and noisy branches). To detect individual worm in clumps, a skeleton analysis and tree reconstruction technique is applied on the binarized image. During experiments, FIGURE 6. Global and color tresholding processes BIB013 . different combination of five population images that include 255 isolated overlapping C. elegan worms are used. Finally, an accuracy of around  .00% is achieved on tested dataset. HSV color mode offers a convenient representation of the color image than RGB, in BIB007 , Hue channel is used to find optimal range occupied by bacilli by applying adaptive thresholding on stained images, in which bacilli appear reddish in color. Then shape descriptors are used for classification. In this work 300 (RGB images of 2045×1536 pixel size) taken from sputum smears of 3 patients are used and results are visually evaluated. In order to automate fully the segmentation process while using multi-level methods, Otsu, entropy and minerror (which involve manual selection of threshold values), the combination of multi-level segmentation methods and method for determining the optimal threshold values are compared in BIB008 - BIB009 . Otsu, entropy and minerror segmentation methods are tested in combination with five methods for threshold values determination (Davies-Bouldin index (DB), Dunn's index (DN), Calinski-Harabasz's index (CH), Xie-Benil index (XB) and index I (I)). The best combination is found to be entropy and DB, since entropy is able to segment all kind of tested images (with single and multiple thresholds), while Otsu and minerror methods show poor segmentation results on single threshold images. In the experiments, 649 images of biofilm are used. These images are subset of two groups, 616 from confocal microscope (resolution of 512 × 512 pixels) and 33 images from an optical microscope with resolution of 1040 × 1392 pixels. In BIB010 , a digital segmentation and classification approach which is capable of identifying individual spiral bacterial cells from complex microbial community is presented, where RGB to gray conversion is applied first, then binarization using global thresholding follows, then geometric features are found before classification using 3δ classifier, k-NN classifier, neural network classifier and fuzzy classifiers. In the experiments, 300 RGB images of Vibrio bacteria, 840 of Spirillium and 140 of Spirochete bacteria are used. Finally 3δ classifier yields an accuracy of 100%. k-NN yields 100% for k = 5. The neural network classifier 100% and fuzzy classifier 100% accuracy. To eliminate contour background in optical microscopic images, a method is proposed in BIB003 . This approach reconstructs the background by calculating surface function minimizing least square error of the sampled image, then Otsu optimal thresholding elects pixels to be representative of the background and bilinear interpolation finds non deterministic background pixels among sample pixels, then 2D cubic fitting method is applied to construct a contour background. Elimination by subtracting approximated background from the original image is applied to achieve a segmented image. In the experiments, the dataset used are gray scale images of Acinetobacter sp and Zygosaccharomyces rouxii of size 640 × 480 pixels. The results of the proposed method are compared with results when applying Otsu's method only. Fig. 7 shows the segmentation results of the two methods. In BIB011 , as quantification of protein aggregation in C. elegans can be used for modeling protein aggregation in human for therapeutic strategies, a system for segmentation and quantification of protein aggregates in C. elegans 3D images is proposed. In this system 2D slices of the images are acquired, then anisotropic diffusion algorithm is applied for denoising, after this follows the global thresholding before morphological operations. Then 3D body reconstruction (using marching cubes algorithm to form cubic grids, followed by tri-linear interpolation between random points in the 2D image and all cubic grids) is performed. Finally quantification of several features on protein aggregates is done (such as volume and area). In the experiments, 150 images of C. elegans are used. The obtained results are consistent with quantitative observation in literature, allowing non biased reliable and high throughput protein aggregates quantification. [55] proposes an approach to segment hepatitis B virus from transmission electron microscopic (TEM) images. The method is based on binarizing the original image to identify the capsids. Since common segmentation or filtering techniques such as basic thresholding or unsharp masking are not suitable, because of the important size of the capsids, the poor contrast, SNR of TEM images, trous wavelets is used for segmentation due to its intrinsic multi-resolution properties, then follows skeletonization and graphical generation to circle the capsids. The tests is done on 9 image series, each series being composed of approximately 40 images of capsids (microscopic hepatitis B images). The ratio of the number of capsids detected by algorithm to that detected manually is used to evaluate the results. The mean ratio (accuracy) obtained is above 80.00% for all sets of tested data. In order to segment TB bacilli from ZN trained tissue with uneven distribution of stain (dye), first global threshold is applied to remove tissue and background from blue dye  , then k-means clustering segments bacilli from background, followed by mean filtering and adaptive thresholding for fine segmentation. 25 RGB images of positive TB stained tissues are used for testing the algorithm. And a segmentation accuracy of 99.00% is achieved. In BIB012 , a scheme to segment metazoa and protozoa from low contrast images of active sludge is proposed, where the image passes through two branches, first branch uses iterative thresholding which extracts the foreground from background and the second branch uses canny edge segmentation which enhances the foreground edges, then the two branches are fused together based on Mallat pyramid wavelet transform to get a fine segmented image. Microscopic gray scale images containing protozoa/metazoa are used during experiments. The results of each stage are shown in Fig. 8 . In order to segment floc and filaments from bright field microscopic images, which are characterized by images with comparatively less illumination near the boundaries, different algorithms based on Otsu thresholding are applied in BIB014 . These algorithms are named, local Otsu I, local Otsu II and local Otsu III. The best one is Otsu III and it is implemented as follows, First, Otsu global thresholding is applied, then bounding boxes are created for all detected objects and around the borders, then Otsu thresholding is applied again at all bounding boxes separately. Then all segmented boxes are rewritten to their pixel positions to get the whole segmented image. Different grayscale images of activated sludge of size 2088 × 1550 pixels are used, during experiments. The counts of flocs in proposed method are compared with manual counts and Otsu III achieves results near to manual counts. In BIB015 , due to the importance of green microalgae for giving signs to deterioration of ecological conditions, a segmentation algorithm of green microalgae images is proposed. In this approach, eigenvalues of the image are found first, then computation of multivariant Gaussian distribution parameters for algae and background is performed, before thresholding to get a binary image. During experiments, 44 RGB (600 × 800 pixel size) images of green algae taken from different species of selenestraceae family are used. The proposed method achieves an accuracy of 92.20%. In BIB016 , a thresholding program which is capable of segmenting irregularly illuminated images from activated sludge is present. Iterative region based Otsu thresholding is applied on bright field microscopic images of activated sludge (it is called iterative region based Otsu because it uses Otsu threshold technique recursively with a selection of foreground region at each iteration until suitable foreground is obtained). In the experiments, 40 RGB images (122 × 960 pixel size) captured from samples of activated sludge are used. The proposed method is compared with other Otsu based methods (iterative Otsu (IO), local adaptive Otsu (LocOtsu) and Otsu). The proposed method outperforms other methods with an accuracy of 98.50%. To enhance diagnosis of malaria quickly, an accurate detection system of Schizont plasmodium falciparum from blood smear is introduced in BIB017 . First median filter is applied to remove salt and pepper noise, followed by color contrast enhancement on RGB images. Then RGB to HIS conversion is performed since a distinct threshold level between blood, parasite and background is easily observed in HIS mode, finally global thresholding is done. In the experiments, 100 RGB microscopic images (size 1600 × 1200 pixels) containing Schizont plasmodium falciparum are used. The segmentation results are evaluated visually as shown in Fig. 9 . Because shape similarity makes it difficult to identify protozoa, an identification system which uses 3D geometric multiple color channel local features is presented in BIB018 , where segmentation is done by decomposing the image into color channels (RGB, R, G, B, gray scale, HSV, H, S, V 