Recent Advances in Morphological Cell Image Analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed

Concavity Point and Skeleton Analysis Algorithm for Detection and Quantization in Heavily Clumped Red Blood Cells

In practice, most hospitals use light microscope to examine the smeared blood for blood quantification. This visual quantification is subjective, laborious and time-consuming. Although automating the process is a good solution, the available techniques are unable to count or ignore the clumpy red blood cells (RBC). Moreover, clumping cell can affect the whole counting process of RBC as well as their accuracy. This paper proposes a new quantization process called concavity point and skeleton analysis (CP-SA) for heavily clump RBC. The proposed methodology is based on induction approach, enhanced lime blood cell by using gamma correction to get the appropriate edges. Then, splitting the clump and single cells by calculating each object area in pixel. Later, the quantification of clumpy cells with the proposed CP-SA method is done. This algorithm has been tested on 556 clump RBC taken from thin blood smear images under light microscope. All dataset images are captured from Hematology Unit, UKM Medical Centre in Kuala Lumpur. On all tested images, the cells of interest are successfully detected and counted from those clump cells. A comparative study and analysis to evaluate the performance of the proposed algorithm in three levels of clump have been conducted. The first level was with two clumps, second level with three clumps and third level with four clumps. The counting number of clump cells has been analyzed using quantitative analysis, resulting in much better results compared to other recent algorithms. The comparison shows that the proposed method gives better precision result at all levels with respect to ground truth: two clump cells (92%), three clump cells (96%) and four clump cells (90%). The results prove that this study has successfully developed a new method to count heavily clump RBC more accurately in microscopic images. In addition, this can be considered as a low-cost solution for quantification in massive examination

Optimizing morphology through blood cell image analysis

Introduction Morphological review of the peripheral blood smear is still a crucial diagnostic aid as it provides relevant information related to the diagnosis and is important for selection of additional techniques. Nevertheless, the distinctive cytological characteristics of the blood cells are subjective and influenced by the reviewer's interpretation and, because of that, translating subjective morphological examination into objective parameters is a challenge. Methods The use of digital microscopy systems has been extended in the clinical laboratories. As automatic analyzers have some limitations for abnormal or neoplastic cell detection, it is interesting to identify quantitative features through digital image analysis for morphological characteristics of different cells. Result Three main classes of features are used as follows: geometric, color, and texture. Geometric parameters (nucleus/cytoplasmic ratio, cellular area, nucleus perimeter, cytoplasmic profile, RBC proximity, and others) are familiar to pathologists, as they are related to the visual cell patterns. Different color spaces can be used to investigate the rich amount of information that color may offer to describe abnormal lymphoid or blast cells. Texture is related to spatial patterns of color or intensities, which can be visually detected and quantitatively represented using statistical tools. Conclusion This study reviews current and new quantitative features, which can contribute to optimize morphology through blood cell digital image processing techniques.Peer ReviewedPostprint (published version

Few-Layered Hexagonal Boron Nitride: Functionalization, Nanocomposites, and Physicochemical and Biological Properties

Hexagonal boron nitride (h-BN) is an analogue of graphite called “white graphene.” In the structure of h-BN, B and N atoms substitute C atoms. The boron and nitrogen atoms are linked via strong B-N covalent bonds and form interlocking hexagonal rings. h-BN is used in different areas due to its interesting physical and chemical properties, e.g., in electronics as an insulator and in ceramics, resins, plastics, and paints. Therefore, boron nitride (BN) is also a popular inorganic compound in cosmetic industry (the highest BN concentration up to 25% can be found in eye shadow formulation). It is also widely used in dental cement production (for dental and orthodontic applications). Boron nitride seems to be suitable for biomedical applications; therefore, the cytotoxicity in vitro and in vivo observations of h-BN nanoplates and novel few-layered h-BN-based nanocomposites are still needed. The short-time studies confirm their low cytotoxicity and suggest that BN can be used as a novel drug delivery system; however, medical application needs additional verification in long-term studies

Evaluation of the effectiveness of simple nuclei-segmentation methods on Caenorhabditis elegans embryogenesis images

BACKGROUND: For the analysis of spatio-temporal dynamics, various automated processing methods have been developed for nuclei segmentation. These methods tend to be complex for segmentation of images with crowded nuclei, preventing the simple reapplication of the methods to other problems. Thus, it is useful to evaluate the ability of simple methods to segment images with various degrees of crowded nuclei. RESULTS: Here, we selected six simple methods from various watershed based and local maxima detection based methods that are frequently used for nuclei segmentation, and evaluated their segmentation accuracy for each developmental stage of the Caenorhabditis elegans. We included a 4D noise filter, in addition to 2D and 3D noise filters, as a pre-processing step to evaluate the potential of simple methods as widely as possible. By applying the methods to image data between the 50- to 500-cell developmental stages at 50-cell intervals, the error rate for nuclei detection could be reduced to ≤ 2.1% at every stage until the 350-cell stage. The fractions of total errors throughout the stages could be reduced to ≤ 2.4%. The error rates improved at most of the stages and the total errors improved when a 4D noise filter was used. The methods with the least errors were two watershed-based methods with 4D noise filters. For all the other methods, the error rate and the fraction of errors could be reduced to ≤ 4.2% and ≤ 4.1%, respectively. The minimum error rate for each stage between the 400- to 500-cell stages ranged from 6.0% to 8.4%. However, similarities between the computational and manual segmentations measured by volume overlap and Hausdorff distance were not good. The methods were also applied to Drosophila and zebrafish embryos and found to be effective. CONCLUSIONS: The simple segmentation methods were found to be useful for detecting nuclei until the 350-cell stage, but not very useful after the 400-cell stage. The incorporation of a 4D noise filter to the simple methods could improve their performances. Error types and the temporal biases of errors were dependent on the methods used. Combining multiple simple methods could also give good segmentations

Neuromuscular disease classification system

Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic pattern

МЕТОДИКА ПОДСЧЕТА ЧИСЛА ЯДЕР КЛЕТОК НА МЕДИЦИНСКИХ ГИСТОЛОГИЧЕСКИХ ИЗОБРАЖЕНИЯХ

In the paper a method of automatical counting the number of cell nuclei in histological images is studied. This operation is commonly used in the diagnostics of various diseases and morphological analysis of cells. In this connection, the procedure of automatical count the number of cell nuclei is a key step in the systems of medical imaging microscopic analysis of histological preparations. The main aim of our work was to develop an efficient scheme of automatic counting cell nuclei based on advanced image processing methods: directional filtering, adaptive image binarization and mathematical morphology. Unlike prior research, the presented approach does not provide segmentation of cell nuclei in the image, but only requires to detect them and count their number. This avoids complex algorithmic calculations and provides good accuracy of counting cell nuclei.The paper describes a series of experiments conducted to assess the effectiveness of the proposed method using the available online database of medical test histological images. Critical parameters defined algorithms, configurable at each stage of image analysis. For each parameter we have defined value ranges, and then realized a selection of optimal values for every parameter and a mutual combination of them. It is based on generally accepted quantitative measures of precision and recall. The results were compared with the state-of-art investigations in this field and demonstrated an acceptable level of accuracy of the proposed method. The software prototype developed during the study can be regarded as an automatic tool for analysis of cell nuclei. The presented approach can be adapted to various problems of analysis of cell nuclei of various organs.В статье исследуется методика автоматического подсчета числа ядер клеток на гистологических изображениях. Эта операция широко применяется при диагностике различных заболеваний и морфологическом анализе клеток. В связи с этим, процедура автоматического подсчета числа ядер клеток является ключевым этапом в системах микроскопического анализа медицинских изображений гистологических препаратов. Основной целью работы была разработка эффективной схемы автоматического подсчета ядер клеток на основе современных методов обработки изображений: направленной фильтрации, адаптивной бинаризации изображений и математической морфологии. В отличие от известных исследований, представленный подход не предусматривает сегментацию ядер клеток на изображении, а лишь предполагает их обнаружение и подсчет их количества. Это позволяет избежать сложных алгоритмических вычислений и обеспечивает хорошую точность подсчета ядер клеток.В работе описан ряд экспериментов, выполненных для оценки эффективности предложенной методики с использованием доступной в интернете тестовый базы медицинских гистологических изображений. Определены критичные параметры алгоритмов, настраиваемые на каждом этапе анализа изображений. Для каждого параметра определен интервал тестируемых значений, а затем реализована процедура выбора не только оптимальных значений каждого параметра, но их из взаимная комбинация, на основе общепринятых количественных оценок точности (Precision) и полноты (Recall). Полученные результаты сравнивались с последними достижениями в данной области и показали приемлемый уровень точности предложенной методики. Прототип программного обеспечения, разработанного в рамках проведенного исследования, можно рассматривать как автоматический инструмент для анализа ядер клеток. Разработанный подход может быть адаптирован к различным задачам анализа ядер клеток различных органов