A novel and effective multistage classification system for microscopic starch grain images

This article presents a novel and effective multistage system for classifying Chinese Materia Medica microscopic starch grain images. The proposed classification system is constructed based on the Gaussian mixture model‐based clustering, the feature assignment algorithm, and the similarity measurement. Several features for each starch grain image are extracted and every class of drug is represented by a set of characteristic features. For each stage of the system, only one feature is chosen and assigned to that stage via the feature assignment algorithm, and the corresponding characteristic features are subdivided into smaller subsets based on clustering techniques. At the final stage, each subset contains a certain class of drugs (with corresponding characteristic features) and similarity measurement is carried out for starch grain classification. Three sets of the current state‐of‐the‐art starch grain features including the granulometric size distribution, the chord length distribution, and the wavelet signature are used to construct the system. Experimental results on a database of 240 images of 24 classes of drugs reveal the superior performance of the multistage system. Comparison with the traditional starch grain classification approaches indicates that our proposed multistage method produces a marked improvement in classification performance. Microsc. Res. Tech. 2009. © 2009 Wiley‐Liss, Inc.     

Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular‐shaped and multicentre particles

Oversegmentation is a major drawback of the morphological watershed algorithm. Here, we study and reveal that the oversegmentation is not only because of the irregular shapes of the particle images, which people are familiar with, but also because of some particles, such as ellipses, with more than one centre. A new parameter, the striping level, is introduced and the criterion for striping parameter is built to help find the right markers prior to segmentation. An adaptive striping watershed algorithm is established by applying a procedure, called the marker searching algorithm, to find the markers, which can effectively suppress the oversegmentation. The effectiveness of the proposed method is validated by analysing some typical particle images including the images of gold nanorod ensembles.     

Automated detection of fluorescent cells in in‐resin fluorescence sections for integrated light and electron microscopy

Integrated array tomography combines fluorescence and electron imaging of ultrathin sections in one microscope, and enables accurate high‐resolution correlation of fluorescent proteins to cell organelles and membranes. Large numbers of serial sections can be imaged sequentially to produce aligned volumes from both imaging modalities, thus producing enormous amounts of data that must be handled and processed using novel techniques. Here, we present a scheme for automated detection of fluorescent cells within thin resin sections, which could then be used to drive automated electron image acquisition from target regions via ‘smart tracking’. The aim of this work is to aid in optimization of the data acquisition process through automation, freeing the operator to work on other tasks and speeding up the process, while reducing data rates by only acquiring images from regions of interest. This new method is shown to be robust against noise and able to deal with regions of low fluorescence.     

Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

The advancement of computer‐ and internet‐based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient–doctor diagnostics is extended to a more robust advanced concept of E‐health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud‐based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape‐based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross‐validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.     

Grain‐oriented segmentation of images of porous structures using ray casting and curvature energy minimization

We segment an image of a porous structure by successively identifying individual grains, using a process that requires no manual initialization. Adaptive thresholding is used to extract an incomplete edge map from the image. Then, seed points are created on a rectangular grid. Rays are cast from each point to identify the local grain. The grain with the best shape is selected by energy minimization, and the grain is used to update the edge map. This is repeated until all the grains have been recognized. Tests on scanning electron microscope images of titanium oxide and aluminium oxide show that their process achieves better results than five other contour detection techniques.     

Automatic detection and analysis of cell motility in phase‐contrast time‐lapse images using a combination of maximally stable extremal regions and Kalman filter approaches

Phase‐contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase‐contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase‐contrast images in time‐lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time‐lapse movies, the MSER‐based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase‐contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time‐consuming large‐scale dynamical analysis of cultured cells.     

Authentication of herbal drug Tukhm‐e‐balango (Lallemantia royleana Benth.) using microscopic,pharmacognostic, and phytochemical characterization

The study is aimed to provide a comprehensive account on authentication of herbal drug named as Tukhm‐e‐balango (Lallemantia royleana Benth.) from the seeds of Ocimum basilicum by using microscopic, pharmacognostic, and phytochemical characterization. The crude medicinal plants and their parts are often adulterated or substituted in market due to improper identification by the consumers while among herbal plant sellers, taxonomic confusion is caused due to morphological similarities of the plant parts and lack of a standard identification system.In microscopy, both herbarium and fresh specimens were studied using qualitative and quantitative morphological characteristics of leaves, seeds, and pollen. For pharmacognosy, solubility, fluorescence, and physicochemical characterizers were analyzed whereas a total phenolic and flavonoids contents was determined in addition to DPPH radical scavenging activity. In current study, microscopic, pharmacognostic, and phytochemical characterization clearly differentiated L. royleana from O. basilicum. The major problem in herbal drug industry is caused due to confusion and controversy of certain synonyms used for more than one or two drugs. Sometimes, under the same common or local name, entirely different taxa are being sold in herbal markets. It is concluded that correct and proper identification of medicinal plants is very crucial to ensure the safety and efficacy of herbal medicines, as many medicinal plants are intentionally or unintentionally adulterated with similar species or varieties. In herbal market, the seeds of L. royleana are adulterated with seeds of O. basilicum due to their similar morphology.     

Comparison of the tracheal systems of Anopheles sinensis and Aedes togoi larvae using synchrotron X‐ray microscopic computed tomography (respiratory system of mosquito larvae using SR‐μCT)

Mosquito‐borne diseases, such as malaria, dengue fever, and Zika virus, are serious global health issues. Vector control may be an important strategy in reducing the mortality caused by these diseases. The respiratory system of mosquito larvae in the water has to inhale atmospheric oxygen as aquatic organisms. In this study, the three‐dimensional (3D) structures of the dorsal longitudinal trunks (DLTs) of the tracheal systems of Anopheles sinensis and Aedes togoi were compared using synchrotron X‐ray microscopic computed tomography. DLT respiratory frequencies were also investigated. Interestingly, the larvae of the two mosquito species exhibit tracheal systems that are both morphologically and functionally distinct. A. sinensis hangs horizontally under the water surface, and has a smaller DLT volume than A. togoi. In contrast, A. togoi hangs upside down using a siphon by fixing its tip to the water surface. The frequency of peristaltic movement in A. togoi is higher than that of A. sinensis. These differences in the structures and breathing behaviors of the respiratory systems of mosquito larvae provide new insights into the tracheal systems of mosquito larvae, which should help develop novel effective control strategies targeting mosquito larvae.