A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose at a given scale is found to be the second derivative of a Gaussian, and it is shown that, provided some simple conditions are satisfied, these primary filters need not be orientation-dependent. Thus, intensity changes at a given scale are best detected by finding the zero values of ∇ 2 G(x, y) * I(x, y) for image I, where G(x, y) is a two-dimen­sional Gaussian distribution and ∇ 2 is the Laplacian. The intensity changes thus discovered in each of the channels are then represented by oriented primitives called zero-crossing segments, and evidence is given that this representation is complete. (2) Intensity changes in images arise from surface discontinuities or from reflectance or illumination bound­aries, and these all have the property that they are spatially localized. Because of this, the zero-crossing segments from the different channels are not independent, and rules are deduced for combining them into a description of the image. This description is called the raw primal sketch. The theory explains several basic psychophysical findings, and the opera­tion of forming oriented zero-crossing segments from the output of centre-surround ∇ 2 G filters acting on the image forms the basis for a physiological model of simple cells (see Marr & Ullman 1979). <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Abstract For the past decade, many image segmentation techniques have been proposed. These segmentation techniques can be categorized into three classes, (1) characteristic feature thresholding or clustering, (2) edge detection, and (3) region extraction. This survey summarizes some of these techniques. In the area of biomedical image segmentation, most proposed techniques fall into the categories of characteristic feature thresholding or clustering and edge detection. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Abstract This paper transmits a FORTRAN-IV coding of the fuzzy c -means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Tuberculosis and other mycobacteriosis are serious illnesses which control is based on early diagnosis. A technique commonly used consists of analyzing sputum images for detecting bacilli. However, the analysis of sputum is 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, a new autofocus algorithm and a new bacilli detection technique is presented with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples. This technique is based on the combined use of some invariant shape features together with a simple thresholding operation on the chromatic channels. Some feature descriptors have been extracted from bacilli shape using an edited dataset of samples. A k-means clustering technique was applied for classification purposes and the sensitivity vs specificity results were evaluated using a standard ROC analysis procedure. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> This review summarizes the microbiology and physiology of "Microthrix parvicella" and the methods of its growth control in activated sludge wastewater treatment plants. This filamentous bacterium is of high interest because of its worldwide involvement in severe bulking and foaming at wastewater treatment plants. We present a critical analysis of physiological and kinetic data on "M. parvicella" and discuss its growth and storage abilities in various environments with the aim of understanding the strategies of this organism to successfully compete with other bacteria in activated sludge. Additionally, this review elaborates on research needs for defining reliable control strategies of bulking and foaming based on key features of "M. parvicella". <s> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Image segmentation is a necessary step in image analysis. Support vector machine is considered a good candidate because of its good generalization performance, especially when the number of training samples is very small and the dimension of feature space is very high. The presented paper investigates image segmentation using support vector machine. Experimental results show that Support vector machine is a promising technique in image segmentation. <s> BIB007 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> A computational approach is presented for modeling and quantifying the structure and dynamics of the nematode C. elegans observed by time-lapse microscopy. Worm shape and conformations are expressed in a decoupled manner. Complex worm movements are expressed in terms of three primitive patterns-peristaltic progression, deformation, and translation. The model has been incorporated into algorithms for segmentation and simultaneous tracking of multiple worms in a field, some of which may be interacting in complex ways. A recursive Bayesian filter is used for tracking. Unpredictable behaviors associated with interactions are resolved by multiple-hypothesis tracking. Our algorithm can track worms of diverse sizes and conformations (coiled/uncoiled) in the presence of imaging artifacts and clutter, even when worms are overlapping with others. A two-observer performance assessment was conducted over 16 image sequences representing wild-type and uncoordinated mutants as a function of worm size, conformation, presence of clutter, and worm entanglement. Overall detected tracking failures were 1.41%, undetected tracking failures were 0.41%, and segmentation errors were 1.11% of worm length. When worms overlap, our method reduced undetected failures from 12% to 1.75%, and segmentation error from 11% to 5%. Our method provides the basis for reliable morphometric and locomotory analysis of freely behaving worm populations. <s> BIB008 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Bacterial colony enumeration is an essential tool for many widely used biomedical assays. However, bacterial colony enumerating is a low throughput, time consuming and labor intensive process since there might exist hundreds or thousands of colonies on a Petri dish, and the counting process is often manually performed by well-trained technicians. In this paper, we introduce a fully automatic yet cost-effective bacterial colony counter. Our proposed method can recognize chromatic and achromatic images and thus can deal with both color and clear medium. In addition, our proposed method can also accept general digital camera images as its input. The whole process includes detecting dish/plate regions, identifying colonies, separating aggregated colonies, and finally reporting consistent and accurate counting results. Our proposed counter has a promising performance in terms of both precision and recall, and is flexible and efficient in terms of labor- and time- savings. <s> BIB009 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> This paper aims to review the development of scientific concepts of microecology and ecology of microbes and the role and functions of beneficial microorganisms in aquaculture and mariculture. Beneficial microorganisms play a great role in natural and man-made aquatic ecosystems based on the co-evolution theory in living biosphere on earth. Their functions are to adjust algal population in water bodies so as to avoid unwanted algal bloom; to speed up decomposition of organic matter and to reduce CODmn, NH3-N and NO2-N in water and sediments so as to improve water quality; to suppress fish/shrimp diseases and water-borne pathogens; to enhance immune system of cultured aquatic animals and to produce bioactive compounds such as vitamins, hormones and enzymes that stimulate growth, thus to decrease the FCR of feed. <s> BIB010 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering. <s> BIB011 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <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> BIB012 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Thick blood smear examination is a necessary part for rapid screening of malaria parasite. Primary diagnosis of malaria by thick smear examination is cheap and highly sensitive, advocated by the World Health Organization (WHO). For the examination of thick blood smear, manpower and time can be reduced by using automated computational techniques. These techniques would facilitate such detection that are usually based on morphology and in few cases on analysis of colors. In this work, we propose a combined algorithm consisting of morphological operations and color based pixel discrimination technique to identify malaria parasites from thick smear images of Plasmodium vivax. Using morphological operation segmentation of cells from thick blood smear image is done and color based pixel discriminator distinguished malaria cells from segmented image. Evaluation of percentage of detection and False Positive Rate (FPR) shows that our proposed algorithm has significantly higher predictive rate, and lower FPR as compared to any existing methodology when tested on same input slides. Importantly the reported method does not need any training set and assumes unsupervised methodology. This makes the portability of the approach at various scales as discussed in the paper. ::: The experiment has been carried out using JSB (Jaswant Singh Battacharya) stained thick blood smear images. It is assuming that our algorithm can identify malaria in less perfect dirty slides. This makes the algorithm more powerful and robust. <s> BIB013 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image. <s> BIB014 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net . <s> BIB015 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists. In this work we synthesize in an unsupervised manner, large histopathology image datasets, suitable for supervised training tasks. We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples. Our main contribution is that the synthetic images are not only realistic, but also representative (in reference styles) and relatively challenging for training task-specific CNNs. We test our method for nucleus segmentation using images from four cancer types. When no supervised data exists for a cancer type, our method without supervision cost significantly outperforms supervised methods which perform across-cancer generalization. Even when supervised data exists for all cancer types, our approach without supervision cost performs better than supervised methods. <s> BIB016 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. MOTIVATION ON AUTOMATIC IMAGE SEGMENTATION OF MICROORGANISMS <s> Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of trainable parameters of deep neural networks however renders them inherently data hungry, a characteristic that heavily challenges the medical imaging community. Though interestingly, with the de facto standard training of fully convolutional networks (FCNs) for semantic segmentation being agnostic towards the `structure' of the predicted label maps, valuable complementary information about the global quality of the segmentation lies idle. In order to tap into this potential, we propose utilizing an adversarial network which discriminates between expert and generated annotations in order to train FCNs for semantic segmentation. Because the adversary constitutes a learned parametrization of what makes a good segmentation at a global level, we hypothesize that the method holds particular advantages for segmentation tasks on complex structured, small datasets. This holds true in our experiments: We learn to segment aggressive prostate cancer utilizing MRI images of 152 patients and show that the proposed scheme is superior over the de facto standard in terms of the detection sensitivity and the dice-score for aggressive prostate cancer. The achieved relative gains are shown to be particularly pronounced in the small dataset limit. <s> BIB017
Microorganisms are very small living organisms which can appear as unicellular, multicellular and acellular types [6] . Some are beneficial and some are harmful to human health and environments. For example, Lactobacteria are beneficial microorganisms, which decompose substances to give nutrients to plants BIB010 . Mmicrothrix parvicella which cause bulking in activated sludge BIB006 , plasmodium and microbacteria tuberculosis which cause malaria and tuberculosis, respectively are harmful microorganims. Good insight into microorgnisms helps in taking advantage of their beneficial side and reduce (eradicate) the negative effects of harmful ones. It is so rare to find microorganisms in isolated environments. Microorganisms are found almost everywhere, in unclean fluids, air, soil and as parasites in other organisms. Most of them have features (for example color and size) which cannot be easily distinguished from their settling environments by naked eyes. Thus manual and (or) automatic processes are required to isolate them. Some of the manual processes involve culturing and staining some samples which are rich in microorganisms  and use of magnification tools such as microscope for viewing. After viewing, analysis can be done manually (traditionally) using experts, or automatically using image segmentation and other image processing techniques. Automatic image segmentation plays the following roles in image analysis: • Feature extraction: Segmentation is usually an important step before feature extraction, as it partitions and isolates the foreground from other parts of the image. Then features like shape, color and texture of the foreground can be extracted easily. For example, BIB004 used adaptive color based thresholding and edge detection method to segment tuberculosis microbes, then extracts shape featured (area, major axis length, minor axis length and perimeter). • Tracking: Image segmentation is very useful when tracking microorganisms in videos which are captured under microscope. Tracking helps to get insight into the mobility, genetic and quantitative characteristics which are needed for research works BIB008 . • Modeling and Analysis: Segmentation is very applicable in modeling and analysis of microorganisms. The results of modeling are used in studying microorganisms development, genetics and their corresponding neurobiology. For example, BIB008 used Neyman Peason decision approach to segment C. elegans for modeling and tracking video images of micro-worms. • Detection: Identification of the desired microorganisms on an image can be done directly after segmentation BIB009 . • Monitoring: It is the process of observing the progress of microorganism over a period of time. Image segmentation is useful in monitoring characteristic changes of the microorganism. For example, BIB012 used Otsu FIGURE 1. Manual segmentation to indicate the presence of parasites on microscopic images performed by biologists where, (a) TB bacteria manually segmented from sputum smears  , (b) Encircled plasmodium (malaria) parasite from blood smears BIB013 . thresholding method for automatic monitoring of floc and filaments in activated sludge water treatments process. Given that image segmentation is of significant importance in various microorganisms tasks including monitoring, detection, tracking and modeling, different approaches for segmentation have been proposed. In this review these methods are grouped into classical and machine learning based methods as listed below: Classical segmentation methods include BIB002 : • Edge based segmentation methods (EBS), such as Canny edge detection  , active contour model BIB005 and Marr Hildreth edge detection BIB001 . • Threshold based segmentation methods (TBS), such as Otsu thresholding  . • Region based segmentation (RBS), such as watershed  . Machine learning based methods, consist of the following: • Supervised machine learning methods (SML), such as support vector machine BIB007 ,  and convolution neural networks like U-net and VGG-16 BIB014 , BIB015 . • Unsupervised machine learning methods (USML), such as fuzzy c-means clustering BIB003 , k-means clustering BIB011 and generative adversarial networks (GANs) based techniques BIB016 - BIB017 .