Red Blood Cell Segmentation Research Articles

Selective Hole Filling of Red Blood Cells for Improved Marker-Controlled Watershed Segmentation.

Manual counting and evaluation of red blood cells with the presence of malaria parasites is a tiresome, time-consuming process that can be altered by environmental conditions and human error. Many algorithms were presented to segment red blood cells for subsequent parasitemia evaluation by machine learning algorithms. However, the segmentation of overlapping red blood cells always has been a challenge. Marker-controlled watershed segmentation is one of the methods that was implemented to separate overlapping red blood cells. However, a high number of overlapped red blood cells were still an issue. We propose a novel approach to improve the segmentation efficiency of marker-controlled watershed segmentation. Local minimum histogram background segmentation with a selective hole filling algorithm was introduced to improve segmentation efficiency of marker-controlled watershed segmentation on a high number of overlapping red blood cells. The local minimum was selected on the smoothed histogram for background segmentation. The combination of selective filling, convex hull, and Hough circle detection algorithms was utilized for the intact segmentation of red blood cells. The markers were computed from the resulted mask, and finally, marker-controlled watershed segmentation was applied to separate overlapping red blood cells. As a result, the proposed algorithm achieved higher background segmentation accuracy compared to popular background segmentation algorithms, and the inclusion of corner details improved watershed segmentation efficiency.

Segmentation of Overlapping Red Blood Cells for Malaria Blood Smear Images by U-Net Architecture

Automated segmentation of red blood cells is a widely applied task in order to evaluate red blood cells for certain diseases. Counting of malaria parasites requires individual red blood cell segmentation in order to evaluate the severity of infection. For such an evaluation, correct segmentation of red blood cells is required. However, it is a difficult task due to the presence of overlapping red blood cells. Existing methodologies employ preprocessing steps in order to segment red blood cells. We propose a deep learning approach that has a U-Net architecture to provide fully automated segmentation of red blood cells without any initial preprocessing. While red blood cells were segmented, irrelevant objects such as white blood cells, platelets and artifacts were removed. The network was trained and tested on 5600 and 600 samples respectively. Segmentation of overlapping red blood cells was achieved with 93.8% Jaccard similarity index. To the best of our knowledge, our results surpassed previous outcomes.

Image analysis and machine learning-based malaria assessment system

Malaria is an important and worldwide fatal disease that has been widely reported by the World Health Organization (WHO), and it has about 219 million cases worldwide, with 435,000 of those mortal. The common malaria diagnosis approach is heavily reliant on highly trained experts, who use a microscope to examine the samples. Therefore, there is a need to create an automated solution for the diagnosis of malaria. One of the main objectives of this work is to create a design tool that could be used to diagnose malaria from the image of a blood sample. In this paper, we firstly developed a graphical user interface that could be used to help segment red blood cells and infected cells and allow the users to analyze the blood samples. Secondly, a Feed-forward Neural Network (FNN) is designed to classify the cells into two classes. The achieved results show that the proposed techniques can be used to detect malaria, as it has achieved 92% accuracy with a database that contains 27,560 benchmark images.

Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not feasible for pixel-level analysis. The convolutional neural network (CNN) model required a large dataset with detailed pixel-level information for the semantic segmentation of red blood cells in the deep learning domain. In current research work, we address these problems by proposing a multi-level deep convolutional encoder-decoder network along with two state-of-the-art healthy and Anaemic-RBC datasets. The proposed multi-level CNN model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixel-level counting of healthy and anaemic-RBC elements along with morphological analysis. For experimental purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks, relevant, complete blood count (CBC), and morphology reports for performance evaluation. The proposed model results were evaluated using crossmatch analysis with ground truth mask by finding IoU, individual training, validation, testing accuracies, and global accuracies using a 05-fold training procedure. This model got training, validation, and testing accuracies as 0.9856, 0.9760, and 0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311, 0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively.

Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease.

Red blood cell (RBC) segmentation and classification from microscopic images is a crucial step for the diagnosis of sickle cell disease (SCD). In this work, we adopt a deep learning based semantic segmentation framework to solve the RBC classification task. A major challenge for robust segmentation and classification is the large variations on the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address these challenges, we apply deformable convolution layers to the classic U-Net structure and implement the deformable U-Net (dU-Net). U-Net architecture has been shown to offer accurate localization for image semantic segmentation. Moreover, deformable convolution enables free-form deformation of the feature learning process, thus making the network more robust to various cell morphologies and image settings. dU-Net is tested on microscopic red blood cell images from patients with sickle cell disease. Results show that dU-Net can achieve highest accuracy for both binary segmentation and multi-class semantic segmentation tasks, comparing with both unsupervised and state-of-the-art deep learning based supervised segmentation methods. Through detailed investigation of the segmentation results, we further conclude that the performance improvement is mainly caused by the deformable convolution layer, which has better ability to separate the touching cells, discriminate the background noise and predict correct cell shapes without any shape priors.

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy.

.Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright–Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.

Red blood cell segmentation by thresholding and Canny detector

A computer visualization system that can detect, count and classify cells in bioimages is needed, and the segmentation of cells plays a vital role in this type of system. In the medical field, blood cell testing is among the most important types of clinical examinations. Manual blood cell examination methods using microscopic devices are more time consuming than automatic methods and require radiologists with more technical skills. However, the development of an effective and fully automatic segmentation process for RBCs (red blood cells) remains challenging. This paper proposes an automatic segmentation algorithm for RBCs that automatically computes the threshold image using boundary-based methods after enhancing the local and global details of the output using morphological operations to segment red blood cells in bioimages. The proposed segmentation method exhibited an average accuracy rate of 87.9% in a public human red blood cell dataset. Furthermore, a comparison of this method with gold segmentation and two other methods typically used for this purpose demonstrated that the proposed method was highly robust and outperformed the other methods.

Extreme learning machine based microscopic red blood cells classification

The digitalization of blood slides introduced pathology to a new era. Despite being the most powerful prognostic tool; automated analysis of microscopic blood smear images is still not used in routine clinical practices as manual pathological image analysis methods are still in use that is tedious, time consuming and subjective to technician dependent variation, furthermore it also needs training and skills. In this work, we present novel method based on extreme machine learning approach for the classification of red blood cells (RBC) images. Segmentation of RBC is initiated with statistical based thresholding to retrieve those pixels which are most relevant to RBC followed by Fuzzy C-means for the image segmentation and boundary detection. Different texture and geometrical features are extracted for the classification of normal and abnormal cells. The classification technique is rigorously evaluated against the dataset to evaluate the accuracy of classifier. We have compared the results with state of the art techniques. So far the proposed technique has produced more promising results as compared to the existing techniques.

Segmentation of RBC in Blood Smear Image using Discrete Shearlet Transform

Blood cell analysis is a critical step in the process of disease analysis. Blood analysis process, usually done manually, has been automated to overcome the cumbersome task of RBC identification, segmentation, and classification. The digital image processing techniques developed in the past few years has made the automation possible. Among all the levels of medical imaging of the blood cells, the segmentation of the blood cell is the most vital task. This paper attempts to develop a new technique to segment an RBC from blood smear images. The proposed method is implemented by extracting a color image from the light microscopic smear image. A green channel is extracted from the color image. Further, image filtering is used to remove noise from the captured image. Finally Red Blood Cell segmentation is implemented using discrete shearlet transform. The proposed method is tested on blood cell images. General Terms Here terms used are Median filtering, Color Image processing, discrete Shearlet transformations and other morphological operations in Medical Image processing for Image Segmentation et. al.

A comparative analysis of watershed and edge based segmentation of red blood cells

Background : Useful information which is helpful in the diagnosis of various disorders is obtained from the analysis of individual blood cells. Aim : To perform a comparative analysis between edge-based segmentation and watershed segmentation on images of the red blood cells. Method : The images to be used for the analysis were gotten from published research works. A database that contains both images was created in the Matlab environment. Edge-based segmentation and watershed segmentation were performed on the images. The edge based segmentation involves finding ridges, lines and contours along the images, while the watershed segmentation involves opening and closing reconstruction of overlapping features in images. MATLAB 2010a was used as the tool box for performing the segmentation process. Result : The value of correlation for segmentation with edge and watershed stood at 0.9499, 0.9198, respectively. Also, in terms of deviation, values for both were 65.846, and 60.317 respectively, and also in terms of area, the values were 39520 and 4467 respectively. Finally, in terms of mean, the values were 187.06 and 17.006. Conclusion : Watershed segmentation outperforms edge based segmentation in terms of image statistics and performance, which can help physician and medical practitioners to identify possible blood disorder. Key words : Watershed, red blood cell, segmentation, edge-based, Matlab, reconstruction

Automated segmentation of multiple red blood cells with digital holographic microscopy

We present a method to automatically segment red blood cells (RBCs) visualized by digital holographic microscopy (DHM), which is based on the marker-controlled watershed algorithm. Quantitative phase images of RBCs can be obtained by using off-axis DHM along to provide some important information about each RBC, including size, shape, volume, hemoglobin content, etc. The most important process of segmentation based on marker-controlled watershed is to perform an accurate localization of internal and external markers. Here, we first obtain the binary image via Otsu algorithm. Then, we apply morphological operations to the binary image to get the internal markers. We then apply the distance transform algorithm combined with the watershed algorithm to generate external markers based on internal markers. Finally, combining the internal and external markers, we modify the original gradient image and apply the watershed algorithm. By appropriately identifying the internal and external markers, the problems of oversegmentation and undersegmentation are avoided. Furthermore, the internal and external parts of the RBCs phase image can also be segmented by using the marker-controlled watershed combined with our method, which can identify the internal and external markers appropriately. Our experimental results show that the proposed method achieves good performance in terms of segmenting RBCs and could thus be helpful when combined with an automated classification of RBCs.

RBCs and Parasites Segmentation from Thin Smear Blood Cell Images

Manually examine the blood smear for the detection of malaria parasite consumes lot of time for trend pathologists. As the computational power increases, the role of automatic visual inspection becomes more important. An automated system is therefore needed to complete as much work as possible for the identification of malaria parasites. The given scheme based on used of RGB color space, G layer processing, and segmentation of Red Blood Cells (RBC) as well as cell parasites by auto-thresholding with offset value and use of morphological processing. The work compare with the manual results obtained from the pathology lab, based on total RBC count and cells parasite count. The designed system successfully detects malaria parasites and RBC cells in thin smear image.