A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> A robot for the automatic screening of cervical smears will be accepted by the cytologists only if it is both accurate and fast. The iteration of neighborhood transformations made it possible to reach these contradictory targets. Several modules of our screening program are described: autofocusing, segmentation of nuclei, selection of alarms, elimination of artefacts, calibration of cells. Results on 1459 slides are presented. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> Cluster division is a critical issue in fluorescence microscopy-based analytical cytology when preparation protocols do not provide appropriate separation of objects. Overlooking clustered nuclei and analyzing only isolated nuclei may dramatically increase analysis time or affect the statistical validation of the results. Automatic segmentation of clustered nuclei requires the implementation of specific image segmentation tools. Most algorithms are inspired by one of the two following strategies: 1) cluster division by the detection of internuclei gradients; or 2) division by definition of domains of influence (geometrical approach). Both strategies lead to completely different implementations, and usually algorithms based on a single view strategy fail to correctly segment most clustered nuclei, or perform well just for a specific type of sample. An algorithm based on morphological watersheds has been implemented and tested on the segmentation of microscopic nuclei clusters. This algorithm provides a tool that can be used for the implementation of both gradient- and domain-based algorithms, and, more importantly, for the implementation of mixed (gradient- and shape-based) algorithms. Using this algorithm, almost 90% of the test clusters were correctly segmented in peripheral blood and bone marrow preparations. The algorithm was valid for both types of samples, using the appropriate markers and transformations. Cytometry 28:289–297, 1997. © 1997 Wiley-Liss, Inc. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> We present an automated method for segmentation of epithelial cells in images taken from ThinPrep scenes by a digital camera in a cytology lab. The method covers both steps of localization of cell objects in low resolution and detection of cytoplasm and nucleus boundary in high resolution. The underlying method makes use of geometric active contours as a powerful tool of segmentation. We also provide the analysis of the connected cells. For this purpose an automatic circular decomposition method is incorporated and adapted to the application by changing its segmentation condition. The results are evaluated numerically and compared with those of previous work in literature. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> Histopathological examination is one of the most important steps in evaluating prognosis of patients with neuroblastoma (NB). NB is a pediatric tumor of sympathetic nervous system and current evaluation of NB tumor histology is done according to the International Neuroblastoma Pathology Classification. The number of cells undergoing either mitosis or karyorrhexis (MK) plays an important role in this classification system. However, manual counting of such cells is tedious and subject to considerable inter- and intra-reader variations. A computer-assisted system may allow more precise results leading to more accurate prognosis in clinical practice. In this study, we propose an image analysis approach that operates on digitized NB histology samples. Based on the likelihood functions estimated from the samples of manually marked regions, we compute the probability map that indicates how likely a pixel belongs to an MK cell. Component-wise 2-step thresholding of the generated probability map provides promising results in detecting MK cells with an average sensitivity of 81.1% and 12.2 false positive detections on average. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> Aiming at the poor real-time characteristics of traditional Ostu for rat sperm image segmentation, we improved this Ostu-based segmentation to reduce its computational time by using Newton iteration. This improved algorithm applies the shape factor s to remove impurities and morphology to mark each sperm with digital tags. The experiments show that this method provides fast and accurate sperm image segmentation and automatic counting of the number of sperms in the images. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> In this letter, we present a novel watershed-based method for segmentation of cervical and breast cell images. We formulate the segmentation of clustered nuclei as an optimization problem. A hypothesis concerning the nuclei, which involves a priori knowledge with respect to the shape of nuclei, is tested to solve the optimization problem. We first apply the distance transform to the clustered nuclei. A marker extraction scheme based on the H -minima transform is introduced to obtain the optimal segmentation result from the distance map. In order to estimate the optimal h-value, a size-invariant segmentation distortion evaluation function is defined based on the fitting residuals between the segmented region boundaries and fitted models. Ellipsoidal modeling of contours is introduced to adjust nuclei contours for more effective analysis. Experiments on a variety of real microscopic cell images show that the proposed method yields more accurate segmentation results than the state-of-the-art watershed-based methods. <s> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> This paper presents two automated methods for the segmentation of immunohistochemical tissue images that overcome the limitations of the manual approach as well as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologies. <s> BIB007 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> An investigation is reported of the identification and measurement of region of interest (ROI) in quantitative phase-contrast maps of biological cells by digital holographic microscopy. In particular, two different methods have been developed for in vitro bull sperm head morphometry analysis. We show that semen analysis can be accomplished by means of the proposed techniques . Extraction and measurement of various parameters are performed. It is demonstrated that both proposed methods are efficient to skim the data set in a preselective analysis for discarding anomalous data. <s> BIB008 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> Sperm movement characteristic plays an important role in male fertility. Computer-aided Sperm Analysis (CASA) systems have tried to provide more accurate information about sperm motility and concentration. At the beginning of this goal, achieving correct sperm trajectory is necessary. On the other hand, sperm tracking is a challenging matter because, sperms have same size and shape, move fast also there is an uncertainty in their motions. Therefore, most of designers have attempted to reduce tracking mistakes especially, when sperms collide with each other. In This paper we try to present a sperm tracking method based on Watershed segmentation and Particle filter estimation which focuses on the sperm collision problem. In contrast of most sperm tracking algorithms based on sperm head orientation, this method tries to track sperms independent of sperm head orientation. Moreover, sperm videos were taken by common camera in order to ease availability in every laboratory. Consequently image sequences have low contrast so, processing section bears more burdens in proportion to the imaging. In this paper, the method is evaluated by tracking 10 sperms which had at least one collision along their paths. The results showed the method is robust in the cluttered scenes. <s> BIB009 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> As an effort to build an automated and objective system for pathologic image analysis, we present, in this paper, a computerized image processing method for identifying nuclei, a basic biological unit of diagnostic utility, in microscopy images of glioma tissue samples. The complete analysis includes multiple processing steps, involving mode detection with color and spatial information for pixel clustering, background normalization leveraging morphological operations, boundary refinement with deformable models, and clumped nuclei separation using watershed. In aggregate, our validation dataset includes 220 nuclei from 11 distinct tissue regions selected at random by an experienced neuropathologist. Computerized nuclei detection results are in good concordance with human markups by both visual appraisement and quantitative measures. We compare the performance of the proposed analysis algorithm with that of CellProfiler, a classical analysis software for cell image process, and present the superiority of our method to CellProfiler. <s> BIB010 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> Automated cervical cancer detection system is primarily based on delineating the cell nuclei and analyzing their textural and morphometric features for malignant characteristics. The presence of cell clusters in the slides have diagnostic value, since malignant cells have a greater tendency to stick together forming clusters than normal cells. However, cell clusters pose difficulty in delineating nucleus and extracting features reliably for malignancy detection in comparison to free lying cells. LBC slide preparation techniques remove biological artifacts and clustering to some extent but not completely. Hence cluster detection in automated cervical cancer screening becomes significant. In this work, a graph theoretical technique is adopted which can identify and compute quantitative metrics for this purpose. This method constructs a cell graph of the image in accordance with the Waxman model, using the positional coordinates of cells. The computed graph metrics from the cell graphs are used as the feature set for the classifier to deal with cell clusters. It is a preliminary exploration of using the topological analysis of the cellgraph to cytological images and the accuracy of classification using SVM showed that the results are well suited for cluster detection. <s> BIB011 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> In this paper we describe an algorithm for accurately segmenting the individual cytoplasm and nuclei from a clump of overlapping cervical cells. Current methods cannot undertake such a complete segmentation due to the challenges involved in delineating cells with severe overlap and poor contrast. Our approach initially performs a scene segmentation to highlight both free-lying cells, cell clumps and their nuclei. Then cell segmentation is performed using a joint level set optimization on all detected nuclei and cytoplasm pairs. This optimisation is constrained by the length and area of each cell, a prior on cell shape, the amount of cell overlap and the expected gray values within the overlapping regions. We present quantitative nuclei detection and cell segmentation results on a database of synthetically overlapped cell images constructed from real images of free-lying cervical cells. We also perform a qualitative assessment of complete fields of view containing multiple cells and cell clumps. <s> BIB012 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> Background : Retroperitoneal tumors constitute a difficult diagnostic category as they are not easily accessible. The advent of image-guided fine-needle aspiration (FNA) has resolved this problem significantly. ::: Aims : We present a short study based on guided aspiration of retroperitoneal tumors, in which we have tried to assess the role of image-guided fine-needle aspiration cytology as a tool for pre-operative diagnosis. ::: Materials and Methods : The study was conducted on patients diagnosed with retroperitoneal masses. FNA was performed under image guidance with the help of ultrasonography and/or computed tomography; smears were prepared and meticulously screened according to a fixed protocol. The results were analyzed to determine sensitivity, specificity, and diagnostic efficacy of cytopathological diagnosis using image-guided FNA techniques. ::: Results: We assessed 38 patients with retroperitoneal masses. In all cases, adequate cellular material was obtained. No major complications were encountered. Statistical analysis was carried out in 35 cases; sensitivity, specificity, and diagnostic accuracy were 100% in these cases. ::: Conclusion: FNA under image guidance should be considered a first-line diagnostic approach for retroperitoneal and other abdominal tumors, although caution should be exercised in case selection. In areas where advanced tests are not available, the cytotechnologist and cytopathologist have a very important role to play in ensuring accurate diagnoses. <s> BIB013 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> HighlightsWe use three different color spaces for detection and segmentation of human sperm head, acrosome and nucleus.We propose a gold-standard built with the cooperation of a referent expert in the field, aiming to create a benchmark set methods for detecting and segmenting sperm cells.We achieve notable improvement in sperm head detection and fewer false positives compared to the state-of-the-art method.Our segmentation approach obtains over 80% overlapping against hand-segmented gold-standard.Our method achieves higher Dice coefficient, lower Hausdorff distance and less dispersion with respect to the results achieved by the state-of-the-art method. Semen analysis is the first step in the evaluation of an infertile couple. Within this process, an accurate and objective morphological analysis becomes more critical as it is based on the correct detection and segmentation of human sperm components. In this paper, we present an improved two-stage framework for detection and segmentation of human sperm head characteristics (including acrosome and nucleus) that uses three different color spaces. The first stage detects regions of interest that define sperm heads, using k-means, then candidate heads are refined using mathematical morphology. In the second stage, we work on each region of interest to segment accurately the sperm head as well as nucleus and acrosome, using clustering and histogram statistical analysis techniques. Our proposal is also characterized by being fully automatic, where a user intervention is not required. Our experimental evaluation shows that our proposed method outperforms the state-of-the-art. This is supported by the results of different evaluation metrics. In addition, we propose a gold-standard built with the cooperation of a referent expert in the field, aiming to compare methods for detecting and segmenting sperm cells. Our results achieve notable improvement getting above 98% in the sperm head detection process at the expense of having significantly fewer false positives obtained by the state-of-the-art method. Our results also show an accurate head, acrosome and nucleus segmentation achieving over 80% overlapping against hand-segmented gold-standard. Our method achieves higher Dice coefficient, lower Hausdorff distance and less dispersion with respect to the results achieved by the state-of-the-art method. <s> BIB014 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> The purpose of this study is to develop an intelligent remote detection and diagnosis system for breast cancer based on cytological images. First, this paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The locations of the cell nuclei in the image were detected with circular Hough transform. The elimination of false-positive (FP) findings (noisy circles and blood cells) was achieved using Otsu's thresholding method and fuzzy c-means clustering technique. The segmentation of the nuclei boundaries was accomplished with the application of the marker-controlled watershed transform. Next, an intelligent breast cancer classification system was developed. Twelve features were presented to several neural network architectures to investigate the most suitable network model for classifying the tumor effectively. Four classification models were used, namely, multilayer perceptron using back-propagation algorithm, probabilistic neural network (PNN), learning vector quantization, and support vector machine (SVM). The classification results were obtained using tenfold cross validation. The performance of the networks was compared based on resulted error rate, correct rate, sensitivity, and specificity. Finally, we have merged the proposed computer-aided detection and diagnosis system with the telemedicine platform. This is to provide an intelligent, remote detection, and diagnosis system for breast cancer patients based on the Web service. The proposed system was evaluated using 92 breast cytological images containing 11502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells and noisy circles. In addition, two benchmark data sets were evaluated for comparison. The results showed that the predictive ability of PNN and SVM is stronger than the others in all evaluated data sets. <s> BIB015 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> C. POTENTIAL RELATED FIELDS FOR APPLICATIONS OF SIMILAR SEGMENTATION METHODS <s> Abstract Breast cancer is the leading type of malignant tumor observed in women and the effective treatment depends on its early diagnosis. Diagnosis from histopathological images remains the "gold standard" for breast cancer. The complexity of breast cell histopathology (BCH) images makes reliable segmentation and classification hard. In this paper, an automatic quantitative image analysis technique of BCH images is proposed. For the nuclei segmentation, top-bottom hat transform is applied to enhance image quality. Wavelet decomposition and multi-scale region-growing (WDMR) are combined to obtain regions of interest (ROIs) thereby realizing precise location. A double-strategy splitting model (DSSM) containing adaptive mathematical morphology and Curvature Scale Space (CSS) corner detection method is applied to split overlapped cells for better accuracy and robustness. For the classification of cell nuclei, 4 shape-based features and 138 textural features based on color spaces are extracted. Optimal feature set is obtained by support vector machine (SVM) with chain-like agent genetic algorithm (CAGA). The proposed method was tested on 68 BCH images containing more than 3600 cells. Experimental results show that the mean segmentation sensitivity was 91.53% (±4.05%) and specificity was 91.64% (±4.07%). The classification performance of normal and malignant cell images can achieve 96.19% (±0.31%) for accuracy, 99.05% (±0.27%) for sensitivity and 93.33% (±0.81%) for specificity. <s> BIB016
Segmentation techniques discussed in this paper are not only suitable for microorganism images, but find application in other related fields such as, in sperm cell segmentation and detection. Sperm cell microscopic images share many similar segmentation challenges with microorganism images including, poor contrast and small size of the foreground related to background. Therefore, same segmentation techniques can be applied in both tasks. Particular examples are, identification of acrosome and nucleus in sperm cells BIB014 , identification of bovine sperm head for morphometry analysis in quantitative phase contrast holographic BIB008 , low contrast sperm tracking BIB009 and rat sperm image segmentation and counting BIB005 . Moreover, VOLUME 7, 2019 TABLE 2. Summary of reviewed works on machine learning based methods (supervised machine learning (SML), unsupervised machine learning (USML), used dataset type, dataset application field and quantity which include class (C), Total (T), training (Tr), validation (V), test (Te), postive (P), negative (N)). the discussed segmentation techniques find potential application in cytopathology and histopathology images when image segmentation is done under cellular level. As these images share some similar characteristics and segmentation challenges with microorganisms such as, overlapping of cells, poor contrast for non stained sample images, superposition of different colors due to unideal distribution of stains on the reaction regions and variation of illumination over the background. Specific examples are, cluster detection in cytology images BIB011 , automatic screening of cytological specimens BIB001 , breast cancer detection and diagnosis BIB015 , BIB016 , segmentation of epithelial cervical cells in images BIB003 , image segmentation of overlapping cervical cells BIB012 , image guided fine needle aspiration of retroperitoneal masses BIB013 , segmentation of cervical and breast clustered cells from histopathology images BIB006 , detection of mitosis and karyorrhexis cells in digitized histological images in prognosis of neuroblastoma BIB004 , segmentation of immunohistochemical tissue images BIB007 , nucleus discrimination in microscopy images of diffuse glioma BIB010 and segmentation of clustered nuclei from bone marrow and peripheral blood specimens images to perform a nonisotopic in situ hybridization of their DNA content BIB002 .