An automated cell viability quantification method for low‐resolution confocal images of closely packed cells based on a modified gradient flow tracking algorithm

Fluorescent‐based live/dead labelling combined with fluorescent microscopy is one of the widely used and reliable methods for assessment of cell viability. This method is, however, not quantitative. Many image‐processing methods have been proposed for cell quantification in an image. Among all these methods, several of them are capable of quantifying the number of cells in high‐resolution images with closely packed cells. However, no method has addressed the quantification of the number of cells in low‐resolution images containing closely packed cells with variable sizes. This paper presents a novel method for automatic quantification of live/dead cells in 2D fluorescent low‐resolution images containing closely packed cells with variable sizes using a mean shift‐based gradient flow tracking. Accuracy and performance of the method was tested on growth plate confocal images. Experimental results show that our algorithm has a better performance in comparison to other methods used in similar detection conditions.     

False Alarm Rate Reduction in the Interpretation of Acoustic to Seismic Landmine Data using Mathematical Morphology and the Wavelet Transform

This paper presents a new technique for false alarm rate reduction to improve the performance of automatic object detection systems that operate on digital images. The proposed technique is applied to the images obtained by a Laser Doppler Vibrometer (LDV) based acoustic to seismic (A/S) system. This technique is based on morphological image processing and the discrete wavelet transform (DWT) of the image data. It begins with a color image transformation to obtain a grayscale image and then a closing operation that enhances the larger objects in the image. After that, the DWT is applied to the closed image. The decimation effect of this DWT helps in the removal of small clutter objects the image. The approximation component resulting from the DWT is used in the detection process. Results from the proposed technique are compared to those obtained using traditional detection techniques.     

Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection—a first study

Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean‐square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering‐based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean‐square error with increasing resolution.     

结核杆菌涂片显微视觉检测系统的自动聚焦

结核杆菌医学涂片大多具有观察区内容稀疏不均匀、杂质较多的特点,使用自动显微镜检方法进行图像采集时,会出现清晰度区分困难、效率低下、甚至聚焦评价失效的问题,为提高自动镜检的效率和准确度,本文自主搭建了显微视觉计算机自动检测系统,对结核杆菌涂片的自动聚焦技术进行系统的研究。首先,对比研究11种常用聚焦函数对结核杆菌镜检玻片图像聚焦评价的优劣,并分析了聚焦成功和失效的原因。在综合分析各聚焦函数对结核杆菌涂片的聚焦效果基础上,提出了一种基于Tenengrad的改进型聚焦评价函数,通过改进内容像素的聚焦权重提高聚焦准确度,优化图像处理算法来提高图像采集效率。实验结果表明:改进型Tenengrad聚焦函数FTen-Q在结核杆菌涂片的各类视野图像评价方面具有高灵敏度和准确度,其聚焦成功率和运算效率分别提高了13.884%和17.616%,可以满足结核杆菌涂片类非均匀涂片的显微视觉自动检测应用要求。     

Quantitative microscopy approach for shape‐based erythrocytes characterization in anaemia

Anaemia is one of the most common diseases in the world population. Primarily anaemia is identified based on haemoglobin level; and then microscopically examination of peripheral blood smear is required for characterizing and confirmation of anaemic stages. In conventional approach, experts visually characterize abnormality present in the erythrocytes under light microscope, and this evaluation process is subjective in nature and error prone. In this study, we have proposed a methodology using machine learning techniques for characterizing erythrocytes in anaemia associated with anaemia using microscopic images of peripheral blood smears. First, peripheral blood smear images are preprocessed based on grey world assumption technique and geometric mean filter for reducing unevenness of background illumination and noise reduction. Then erythrocyte cells are segmented using marker‐controlled watershed segmentation technique. The erythrocytes in anaemia, such as, tear drop, echinocyte, acanthocyte, elliptocyte, sickle cells and normal erythrocytes cells have been characterized and classified based on their morphological changes. Optimal subset of features, ranked by information gain measure provides highest classification performance using logistic regression classifier in comparison with other standard classifiers.     

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears

Visual inspection for the quantification of malaria parasitaemiain (MP) and classification of life cycle stage are hard and time taking. Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k‐nearest neighbor, Naïve Bayes and multi‐class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non‐infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing .     

Comparison of two automatic cell‐counting solutions for fluorescent microscopic images

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple‐to‐use cell‐counting programs that also allow users to correct the detection results. In this paper, we present the Cellcounter and Learn 123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user‐friendly interface. Although Cellcounter is based on predefined and fine‐tuned set of filters optimized on sets of chosen experiments, Learn 123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. Cellcounter also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R² < 0.9), although Cellcounter had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours to minutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, Cellcounter overlay extension also enables fast analysis of related images that would otherwise require image merging for accurate analysis, whereas Learn 123's evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings.     

Structure similarity‐guided image binarization for automatic segmentation of epidermis surface microstructure images

Partitioning epidermis surface microstructure (ESM) images into skin ridge and skin furrow regions is an important preprocessing step before quantitative analyses on ESM images. Binarization segmentation is a potential technique for partitioning ESM images because of its computational simplicity and ease of implementation. However, even for some state‐of‐the‐art binarization methods, it remains a challenge to automatically segment ESM images, because the grey‐level histograms of ESM images have no obvious external features to guide automatic assessment of appropriate thresholds. Inspired by human visual perceptual functions of structural feature extraction and comparison, we propose a structure similarity‐guided image binarization method. The proposed method seeks for the binary image that best approximates the input ESM image in terms of structural features. The proposed method is validated by comparing it with two recently developed automatic binarization techniques as well as a manual binarization method on 20 synthetic noisy images and 30 ESM images. The experimental results show: (1) the proposed method possesses self‐adaption ability to cope with different images with same grey‐level histogram; (2) compared to two automatic binarization techniques, the proposed method significantly improves average accuracy in segmenting ESM images with an acceptable decrease in computational efficiency; (3) and the proposed method is applicable for segmenting practical EMS images. (Matlab code of the proposed method can be obtained by contacting with the corresponding author.)     

Image enhancement for increased dot-counting efficiency in FISH

Background: The most commonly used molecular cytogenetic technique is fluorescence in situ hybridization (FISH). It has been widely applied in many areas of diagnosis and research, including pre‐natal and post‐natal screening of chromosomal aberrations, pre‐implantation genetic diagnosis, cancer cytogenetics, gene mapping, molecular pathology and developmental molecular biology. The analysis of FISH images consists of detecting fluorescent dots, after which the number of dots per cell can be counted or their relative positions can be measured. A major impediment in the analysis of FISH specimens is signal (dot) quality, which is influenced by the hybridization efficiency and/or the sensitivity of the camera that records the images. Method: In this paper, we present an approach to improve the efficiency of detecting fluorescent signals in FISH images by recovering the radiance map of the camera. This allows us to generate a high‐dynamic‐range image wherein an extended range of the sample radiance captured by the camera can be visualized at distinct intensity values. The resulting higher‐order numeric complexity of the transformed image is adjusted (or simplified) by examining the intensity distribution in each of the three colour channels (red, green and blue), and remapping the intensity values to generate a high‐contrast image with a lower‐order (compressed) dynamic range. The remapping is based on a criterion that optimizes the detection of the hybridized signals, allowing attenuation of saturated intensity values while amplifying low‐intensity signals. Results: A simple dot‐counting algorithm is used to automatically process 2000 FISH images. The images are taken for lymphocytes from cultured blood specimens for cytogenetic testing. Images are manually analyzed by an expert to obtain ground truth for dot counts. A quantitative analysis is performed by comparing results of automated dot detection on images before and after enhancement with the developed algorithms. In addition, common errors in dot counting due to split dots, dust, poor segmentation and overlapping signals are analyzed and the robustness of the developed approach against these errors evaluated. It is observed that dot‐detection efficiency is increased by an average of 9% across all colour channels while reducing errors in missed and false dot counts. Conclusions: Our proposed method and results demonstrate that dot‐counting specificity and sensitivity can be improved by pre‐processing and enhancing the image using the radiance curve of the camera and generating a high‐contrast, remapped high‐dynamic‐range image prior to using any algorithm for dot counting.     

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy remains the primary diagnostic laboratory test. However, microscopic examination is always time‐consuming and tedious. Therefore, an effective computer‐aided image identification system is needed to provide timely assistance in diagnosis. The current identification system usually suffers from complex color variations of the images, resulting in plentiful of false object detection. To overcome the dilemma, we propose a two‐stage Mycobacterium tuberculosis identification system, consisting of candidate detection and classification using convolution neural networks (CNNs). The refined Faster region‐based CNN was used to distinguish candidates of M. tuberculosis and the actual ones were classified by utilizing CNN‐based classifier. We first compared three different CNNs, including ensemble CNN, single‐member CNN, and deep CNN. The experimental results showed that both ensemble and deep CNNs were on par with similar identification performance when analyzing more than 19,000 images. A much better recall value was achieved by using our proposed system in comparison with conventional pixel‐based support vector machine method for M. tuberculosis bacilli detection.