A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. AUTOMATIC MICROSCOPIC MICROORGANISM IMAGE SEGMENTATION METHODS <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> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. AUTOMATIC MICROSCOPIC MICROORGANISM IMAGE SEGMENTATION METHODS <s> Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semi- or full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future development trend in this interdisciplinary field. In addition, the frequently-used CBMIA methods are further analysed to find technological common points and potential reasons. <s> BIB002
Segmentation is a process of partitioning image into some non-intersecting regions such that each region is homogeneous and the union of two adjacent regions is heterogeneous  . Traditional approaches for segmentation are over-weighed by their drawbacks like time consuming and inconsistency. To get rid of such drawbacks, the application of automatic segmentation methods come to hand. Automatic image segmentation means, no involvement of human in the processes of partitioning and identification of the desired part on an image unless in the algorithm design process. Computer algorithms are used to partition images into segments so that analysis can be done. Contrary to traditional approaches, automatic image segmentation is faster, consistent, accurate and generally cheap. For example, in medical field, automatic image segmentation helps in fast, reliable and consistent diagnosis of diseases, identification and classification of pathogen, which contributes to saving lives BIB001 . Due to its advantages, so many recent researches are performed on segmentation of different microorganism images. The development trend over recent decades is shown in Fig. 2 . Owing to the role of microorganisms in our life circle and many researches being focused on automatic image segmentation, we attempt to write this review paper aiming at pinning out the most frequently used methods and their technical advantages to specific microorganism image segmentation challenges. For a grasp on categories of image segmentation methods as described in this review, Fig. 3 shows important steps in automatic image segmentation pipeline, which include data acquisition, color conversion, image enhancement, denoising, image segmentation techniques and their respective categories and subcategories. From the flow chart in Fig. 3 we can find that: Stage (1): Is acquisition of microorganism images which is normally done from samples under microscope. Microorganisms can be from agricultural field, food processing field, medical or other fields. Most common classes of microorganisms of interest are bacteria, archaea, protists, animals, fungi, plants and virus BIB002 . Stage (2): Image conversions and enhancement take place depending on visual and contrast characteristic of microorganism against background on image. Common conversions are RGB to gray scale, RGB to HSV, RGB to LAB, RGB to HSI and RGB to CMYk color modes. Denoising is also done at this stage using one of the existing filters like Gaussian filter, mean, median filters and other possible denoising techniques depending on the nature of noise on an image. Stage (3): Is the segmentation phase, in which microorganisms are isolated from background and other artifacts. Suitable methods sorted between two categories (a) or (b), classical or machine learning methods can be applied for segmentation. Classical methods include subcategories ((a)iiii) which are threshold based, like Otsu (which segments images on the bases of intensity values to get a binary image), region based and edge based methods like Sobel and Canny techniques. Threshold and edge based methods are normally applied to gray scale images. The most common supervised learning methods ((b)(i)) are convolution neural networks, support vector machine and naïve Bayes classifiers. Neural networks do not need much preprocessing like RGB to gray conversion as they are capable of working directly on the coloured images, also they do not need pre-defined features to enable segmentation contrary to SVM which needs pre defined features. The most common unsupervised methods for segmentation ((b)(ii)) are similarity based clustering methods like fuzzy and k-means related ones.