A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> VOLUME 7, 2019 <s> In this paper, we tackle the problem of finding microorganisms in bright field microscopy images, which is an important and challenging step in various tasks, like classifying soil textures. Apart from bacteria or fungi, these images can contain impurities such as sand particles, which increase the difficulty of microbe detection. Following a semantic segmentation approach, where a label is inferred for each pixel, we achieve encouraging classification results on a database containing five different types of microbes. We review and evaluate multiple techniques including segment classification, conditional random field models, and level set approaches. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> VOLUME 7, 2019 <s> Ziehl-Neelsen staining in sputum smear slides of pulmonary tuberculosis disease causes the sputum images become complex. The clinicians feel hard to examine sputum slide manually because there is no staining standardization. For helping the clinicians, this research developed new algorithm which did segmentation to separate the tuberculosis bacteria images from the background images. So that, the tuberculosis bacteria appear well. Several methods have been performed in this research. There were adaptive color thresholding, K-means clustering and K-nearest neighbors to improve the performance of color segmentation. All processing were done in the Commission Internationale de l'Eclairage Lab (CIELAB) color space. K-nearest neighbors method gave the best accuracy 97.90%, but has not been able to give good result on the whole image and need long computational time in learning process. Therefore, this research modified K-means clustering using patch technique for color image segmentation in the image of pulmonary tuberculosis sputum. The weakness of local optima in the K-means clustering repaired with a patch technique, as well as the learning process to get the patch pattern which is used as a reference to the new data. This method gave good segmentation accuracy 97.68% and fast computational process. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> VOLUME 7, 2019 <s> Tuberculosis is an infectious threatening disease caused by mycobacterium bacillus, affected one third of world's population. In most of the developing countries like India, the simple and low cost sputum smear microscopy is commonly used as a diagnostic tool for TB. This paper presents an automatic segmentation and classification of tuberculosis bacillus from Ziehl Neelsen stained sputum images acquired using bright field microscope. Fuzzy local Information C-Means clustering algorithm is proposed for segmentation. After segmentation a novel hybrid feature extraction technique is proposed and finally classification of bacilli is carried out using least square support vector machines. Investigational result shows that our work is an efficient approach which assists pathologists in decision making. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> VOLUME 7, 2019 <s> In microscopy, new super-resolution methods are emerging that produce three-dimensional images at resolutions ten times smaller than that provided by traditional light microscopy. Such technology is enabling the exploration of structure and function in living tissues such as bacterial biofilms that have mysterious interconnections and organization. Unfortunately, the standard tools used in the image analysis community to perform segmentation and other higher-level analyses cannot be applied naively to these data. This paper presents Bact-3D, a 3D method for segmenting super-resolution images of multi-leveled, living bacteria cultured in vitro. The method incorporates a novel initialization approach that exploits the geometry of the bacterial cells as well an iterative local level set evolution that is tailored to the biological application. In experiments where segmentation is used as precursor to cell detection, the Bact-3D matches or improves upon the Dice score and mean-squared error of two existing methods, while yielding a substantial improvement in cell detection accuracy. In addition to providing improvements in performance over the state-of-the-art, this report also characterizes the tradeoff between imaging resolution and segmentation quality. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> VOLUME 7, 2019 <s> Tuberculosis is one of an infectious threatening disease affected one third of world's population but its fatality rate can controlled by diagnosing and treating at an early stage itself. Ziehl-Neelsen stained sputum smear microscopy is the most popular diagnosis method in developing countries. The stained images do not always have the sufficient contrast and hence the clinicians feel hard to inspect bacteria on it. Our Proposed algorithm automatically detects and segments the tuberculosis bacteria from background using improved fuzzy local information C means (IFLICM) clustering algorithm that overcomes the drawbacks of existing fast-generalized fuzzy C means clustering algorithm and improves the performance of clustering. Experimental result shows that IFLICM algorithm is efficient, robust to noise and a feasible alternative while comparing with traditional fuzzy based algorithms by giving an average segmentation accuracy of 96.05 on sputum image dataset. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> VOLUME 7, 2019 <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> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> VOLUME 7, 2019 <s> Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios. In this paper, we go even further by proposing a fully unsupervised approach for segmentation applications with prior knowledge of the objects' shapes. We propose and investigate different strategies to generate simulated label data and perform image-to-image translation between the image and the label domain using an adversarial model. Specifically, we assess the impact of the annotation model's accuracy as well as the effect of simulating additional low-level image features. For experimental evaluation, we consider the segmentation of the glomeruli, an application scenario from renal pathology. Experiments provide proof of concept and also confirm that the strategy for creating the simulated label data is of particular relevance considering the stability of GAN trainings. <s> BIB007
The achieved results show that k-mean clustering (k = 3) outperforms other methods with an accuracy of 99.49%. In order to increase segmentation performance of microbe from bright field microscopic images, two approaches are evaluated on the use of condition random fields (CRF) technique before pixel wise classification (tree based Gaussian process classifier) stage in segmentation pipeline in BIB001 . First approach uses mean shift as unsupervised method, followed by pixel wise classifier decision (tree based Gaussian process classifier). Second approach uses extracted features and applies them on probabilistic Parzen classifier, followed by tree based Gaussian process classifier. In the experiments, RGB images of microbes are used, in which each image contains 40-470 microbes among the following Bacillus subtilis, Escberichia coli, Micrococcus luteus, Staphylococcus epidermidis and yeast. The obtained results show that both approaches increase performance with the use of CRF with an average accuracy of 80.00%. In BIB002 , a segmentation system of pulmonary tuberculosis bacteria is introduced. First, the image is resized to n × n square matrix, then it is divided into small square patches finally each patch is independently segmented using k-mean clustering and the results are stored in dictionary, then reconstruction of the segmented image is done from segmented patches. Images patches are used to avoid local minima problem which mostly face k-mean clustering. To evaluate the performance of the proposed method, 400 RGB images of size 640 × 360 pixels are used. Finally the segmentation accuracy of 97.68% is achieved. In BIB003 , segmentation and classification algorithm is applied so as to automate the TB diagnosis process. Segmentation is performed using fuzzy local information c-means (FLICM) clustering. FLICM is derived from fuzzy c-means clustering and it is an efficient algorithm, which is more robust to noise parameters (corrupted pixels) within an image, using its objective function, thus no any preprocessing is needed. After segmentation, features are extracted using Hu moments and SIFT algorithm and finally classification is done using least square support vector machines (LSSVM). In the experiments, 299 images (512 × 512 pixels) captured from ZN stained sputum under digital microscope are used. The results of 95.10% accuracy, is achieve for segmentation. A segmentation system capable of segmenting 3D images containing bacteria biofilms is introduced in BIB004 , where in order to identify and segment the individual bacteria in 3D, each layer of 3D image is segmented differently using Bact-3D algorithm and finally reconstruct the segmented 3D volume as shown in Fig. 23 . The performance of the proposed algorithm, is tested using 3D biofilm images and an accuracy of 99.81% is obtained. In  , an automatic segmentation method is used to detect malaria parasites from stained blood smears containing four types of Plasmodium (falciparum, malariae, ovale and vivax). The algorithm uses green channel which is dominant channel for plasmodium in RGB image, then Gamma correlation is applied for image enhancement, followed by noise FIGURE 23. Flow chart of Bact-3D algorithm BIB004 . removal by Gaussian filter, then edge enhancement and fuzzy c-means clustering are applied for segmentation. The final segmentation results achieve an accuracy of 98.26%. In order to effectively segment TB bacteria from microscopic images of sputum samples, a clustering based method is presented in BIB005 . Where an improved fuzzy local information c-means (IFLICM) clustering, is used for segmentation. IFICM is capable to combine both local spatial and local gray level information in a fuzzy way to preserve robustness and noise insensitiveness. It manages the effect of neighborhood pixels depending on their distance from the central pixel. It is derived and improved from fast generalized fuzzy c-means and fuzzy c-means algorithms. During experiments, 99 RGB images of ZN stained bacilli are used in which, 29 of them are resized into 512×512 pixels and used for training the system and 70 images are used for testing. The performance accuracy of 96.05% is achieved. Although, to the best of our knowledge, generative adversarial networks (GANs) have never been applied directly to segmentation of microorganisms, they show great potential in enhancing segmentation of microscopic images, which share similar characteristics and segmentation challenges as microorganisms. Such as segmentation of prostate cancer cells from low contrast images and synthesis of more dataset to tackle the challenge of scarcity in dataset, especially in deep learning related segmentation methods BIB006 , BIB007 . Thus, a brief overview on GANs with related works is given in Sec. IV-D.