Leukocyte nucleus segmentation and nucleus lobe counting

<p>Abstract</p> <p>Background</p> <p>Leukocytes play an important role in the human immune system. The family of leukocytes is comprised of lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Any infection or acute stress may increase or decrease the number of leukocytes. An increased percentage of neutrophils may be caused by an acute infection, while an increased percentage of lymphocytes can be caused by a chronic bacterial infection. It is important to realize an abnormal variation in the leukocytes. The five types of leukocytes can be distinguished by their cytoplasmic granules, staining properties of the granules, size of cell, the proportion of the nuclear to the cytoplasmic material, and the type of nucleolar lobes. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.Biomedical technologists can currently recognize abnormal leukocytes using human eyes. However, the quality and efficiency of diagnosis may be compromised due to the limitations of the biomedical technologists' eyesight, strength, and medical knowledge. Therefore, the development of an automatic leukocyte recognition system is feasible and necessary. It is essential to extract the leukocyte region from a blood smear image in order to develop an automatic leukocyte recognition system. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.</p> <p>Results</p> <p>The purpose of this paper is to contribute an automatic leukocyte nuclei image segmentation method for such recognition technology. The other goal of this paper is to develop the method of counting the number of lobes in a cell nucleus. The experimental results demonstrated impressive segmentation accuracy.</p> <p>Conclusions</p> <p>Insensitive to the variance of images, the LNS (Leukocyte Nuclei Segmentation) method functioned well to isolate the leukocyte nuclei from a blood smear image with much better UR (Under Segmentation Rate), ER (Overall Error Rate), and RDE (Relative Distance Error). The presented LC (Lobe Counting) method is capable of splitting leukocyte nuclei into lobes. The experimental results illuminated that both methods can give expressive performances. In addition, three advanced image processing techniques were proposed as weighted Sobel operator, GDW (Gradient Direction Weight), and GBPD (Genetic-based Parameter Detector).</p

Digital blood image processing and fuzzy clustering for detection and classification of atypical lymphoid B cells

Automated systems for digital peripheral blood (PB) cell analysis operate most effectively in non-pathological samples. The paper deals with the automatic classification of atypical lymphoid cells using digital image processing. The problem has been approached through a 3-step procedure: 1) Watershed segmentation of nucleus, cytoplasm and peripheral cell zone; 2) feature extraction for each region; and 3) classification using fuzzy c-means. The paper has proposed a new methodology that has been able to automatically classify with high precision three types of lymphoid cells: normal, Hairy Cell Leukemia cells and Chronic Lymphocytic Leukemia cells. This methodology, combining human medical expertise with mathematical and engineering tools, may contribute to improve the efficiency of the hematology laboratory.Peer Reviewe

Fluorescent Imaging on a Microfluidics Chip for Quantification of Leukocyte Count

We have demonstrated a method for conducting a leukocyte count in whole blood using a microfluidics chip and epi-fluorescence setup. Leukocyte counts provide physicians with a wealth of information about a patient’s medical condition and as such are routinely completed for many hospital visits. Miniaturization of this diagnostic tool may enable physicians to provide healthcare in resource-limited settings, where patients would otherwise not receive this test. The microfluidics chip was fabricated in polydimethylsiloxane (PDMS) using soft-film lithography. Following further processing and cleaning, the PDMS mold is exposed to UV-ozone for surface activation, and then sealed with a glass coverslip to create an enclosed chip. The epi-fluorescence microscope was constructed using a blue LED light source, excitation and emission filters, dichroic mirror, and objective lens. Prior to imaging leukocytes in whole blood, the optimal linear flow velocity in the microfluidics channel had to be determined to achieve minimal motion blur and sufficient signal-to-noise ratio. This was done by conducting a series of flow rate experiments in which fluorescent microspheres were seeded in phosphate-buffered saline (PBS) and pumped at various volumetric pump rates while simultaneously imaged with the epi-illuminating fluorescence microscop. These images were analyzed using ImageJ to determine average linear velocity of bead flow as it passed the image sensor. Values from this experiment were used to pump leukocytes at an optimal rate for image acquisition. Minimal pre-processing of the sample was completed with an anticoagulation agent, which prevents clogging within the channel, and proflavine, a fluorophore used to stain the nuclei of the leukocytes for imaging. The processed blood sample was then pumped into the chip and imaged simultaneously. Image data was gathered and processed to separate between populations of leukocytes based on nuclear morphology. In this particular system, a 3-part differential can be completed to distinguish between monocytes, lymphocytes, and granulocytes

Threshold estimation based on local minima for nucleus and cytoplasm segmentation

Background Image segmentation is the process of partitioning an image into separate objects or regions. It is an essential step in image processing to segment the regions of interest for further processing. We propose a method for segmenting the nuclei and cytoplasms from white blood cells (WBCs). Methods Initially, the method computes an initial value based on the minimum and maximum values of the input image. Then, a histogram of the input image is computed and approximated to obtain function values. The method searches for the first local maximum and local minimum from the approximated function values in the order of increasing of knots sequence. We approximate the required threshold from the first local minimum and the computed initial value based on defined conditions. The threshold is applied to the input image to binarize it, and then post-processing is performed to obtain the final segmented nucleus. We segment the whole WBC before segmenting the cytoplasm depending on the complexity of the objects in the image. For WBCs that are well separated from red blood cells (RBCs), n thresholds are generated and then produce n thresholded images. Then, a standard Otsu method is used to binarize the average of the produced images. Morphological operations are applied on the binarized image, and then a single-pixel point from the segmented nucleus is used to segment the WBC. For images in which RBCs touch the WBCs, we segment the whole WBC using SLIC and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC. Results The method is tested on two different public data sets and the results are compared to the state of art methods. The performance analysis shows that the proposed method segments the nucleus and cytoplasm well. Conclusion We propose a method for nucleus and cytoplasm segmentation based on the local minima of the approximated function values from the image’s histogram. The method has demonstrated its utility in segmenting nuclei, WBCs, and cytoplasm, and the results are satisfactory.publishedVersio

Epigenetic function of Granulocytic nuclei? Designing a new line of research

Granulocytes share the common feature of having a lobulated nucleus, a fact whose function has yet to be discussed in depth. This hypothesis suggests that the division of the nuclei follows an epigenetic purpose, separating genes into compartments with different regulatory mechanisms, which may be due to intrinsic factors like regulatory RNA or extrinsic factors like proteins. This paper describes the outlines of a line of research for both the initial testing to test the hypothesis and the following descriptive studies, including potential clinical uses in the prognosis or diagnosis of diseases that present dysregulation of the number of lobes. The chosen approach is to study the pattern of distribution (random or otherwise) of genes amongst the lobes

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 countinghavebeendeveloped sofar,butmostof 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 LEARN123 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, LEARN123 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 tominutes. 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 thatwouldotherwise require imagemergingforaccurateanalysis, whereas LEARN123’s evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings

Selected themes of histology, cytology and embryology core

CYTOLOGYEMBRYOLOGYHISTOLOGYГИСТОЛОГИЯУЧЕБНЫЕ ПОСОБИЯЦИТОЛОГИЯЭМБРИОЛОГИЯThe study manual includes topics on histology, cytology and embryology core