A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. ANALYSIS ON CLASSICAL METHODS <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> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. ANALYSIS ON CLASSICAL METHODS <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> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. ANALYSIS ON CLASSICAL METHODS <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> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. ANALYSIS ON CLASSICAL METHODS <s> Every year, one million people dies from Hepatitis B virus. As for other viruses, its genetic material is enclosed by a capsid whose segmentation and classification is essential. In this paper, we present a novel capsid segmentation technique which is a combination of a “a trous” wavelet process (for background filtering) and a graph-based structure (for segmentation and classification). Capsids were acquired in transmission electron microscopy (TEM) as a set of 9 series of 40 images each. Our technique achieved as much as 80% of capsid detection and classification for 8 of the series, reaching more than 90% for 5 of them. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. ANALYSIS ON CLASSICAL METHODS <s> The present paper describes the study of the threshold techniques in image segmentation. Image segmentation is one of the fundamental approaches of the digital image processing. Image segmentation is used widely in many applications. Several general purpose algorithms and techniques have been developed for image segmentation. Segmentation applications are involving detection, recognition and measurement of features. The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application. Segmentation techniques can be classified as either contextual or non-contextual . Thresholding is a Non-Contextual Approach. This method is based on a threshold value to turn a gray-scale image into a binary image. In Histogram Dependent Technique, a histogram is computed from all of the pixels in the image and this paper enumerates and reviews a comparative performance of threshold technique as Histogram Dependent Technique (HDT) based on Global Threshold, Local Threshold and Adaptive Threshold one another. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. ANALYSIS ON CLASSICAL METHODS <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> BIB006
In classical methods, as observed in Table. 1, threshold based segmentation (TBS) methods are the most frequently used techniques, this is because threshold based segmentation has many alternatives such as global, multithreshold, Otsu and adaptive thresholding. This accounts for it being suitable for many different segmentation challenges present in microorganism images. For example, most of microscopic images have uneven illumination on the backgrounds due to the nature of environments where microorganisms are found, under this case adaptive and multithreshold techniques are suitable (adaptive thresholding uses different threshold values for different local areas on image) BIB005 . Low (poor) contrast ratio between background and foreground, is also a big challenge faced in microorganism images BIB004 , in which histogram of images have no clear defined valley point for thresholding. This challenge mostly appears to images from conventional light microscopes which are commonly used in many third world countries  ,  , however, this can be resolved using iterative thresholding  , wavelet transform BIB004 and color based thresholding. A clear example can be seen in  where color and global adaptive thresholding are used to threshold low contrast image from conventional microscope. In most cases when staining is applied to the tested sample then capturing images, the foreground and background are sufficiently distinct, in this case global, histogram based and Otsu thresholds are suitable for segmentation. For example in  Otsu thresholding is applied on stained sample images, in  and BIB001 global thresholding is applied on stained sample images. Moreover, threshold based techniques are the easiest methods to implement. This is why they are mostly used  . Furthermore, they don't require many datasets, thus suitable for segmentation of medical related microorganisms where confidentiality leads to scarcity of dataset  , BIB002 , BIB004 . On top of that, threshold based methods have simple algorithms which can be run by low speed computers and are incorporated in software libraries which can be utilized by almost all common programming languages and platforms, such as python and Matlab, contrary to many machine learning models which need high speeds, high number of dataset and can run well only in some special IDEs. For example in  , BIB003 , BIB006 Matlab is used during experiments. Additionally, threshold segmentation techniques are generally inexpensive and computationally faster  . All these make threshold based methods being suitable for most of segmentation challenges in microorganism images.