scholarly journals A Novel Approach for Objective Assessment of White Blood Cells Using Computational Vision Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Cesar Mauricio Rodríguez Barrero ◽  
Lyle Alberto Romero Gabalan ◽  
Edgar Eduardo Roa Guerrero
Keyword(s):  
Blood Cells ◽  
Physiological State ◽  
Objective Assessment ◽  
White Blood Cells ◽  
Morphological Characteristics ◽  
Coefficient Of Determination ◽  
Technological Support ◽  
Novel Approach ◽  
Base Functions

In the field of medicine, the analysis of blood is one of the most important exams to determine the physiological state of a patient. In the analysis of the blood sample, an important process is the counting and classification of white blood cells, which is done manually, being an exhaustive, subjective, and error-prone activity due to the physical fatigue that generates the professional because it is a method that consumes long laxes of time. The purpose of the research was to develop a system to identify and classify blood cells, by the implementation of the networks of Gaussian radial base functions (RBFN) for the extraction of its nucleus and subsequently their classification through the morphological characteristics, its color, and the distance between objects. Finally, the results obtained with the validation through the coefficient of determination showed an overall accuracy of 97.9% in the classification of the white blood cells per individual, while the precision in the classification by type of cell evidenced results in 93.4% for lymphocytes, 97.37% for monocytes, 79.5% for neutrophils, 73.07% for eosinophils, and a 100% in basophils with respect to the professional. In this way, the proposed system becomes a reliable technological support that contributes to the improvement of the analysis for identification of blood cells and therefore would benefit the low-level hematology establishments as well as to the processes of research in the area of medicine.

scholarly journals New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

2021 ◽  
Author(s):  
Eslam Tavakoli ◽  
Ali Ghaffari ◽  
Seyedeh-Zahra Mousavi Kouzehkanan ◽  
Reshad Hosseini
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Dice Similarity Coefficient ◽  
Second Phase ◽  
Peripheral Smear ◽  
Color Features ◽  
Svm Model ◽  
Extraction Algorithm ◽  
Processing Techniques

This article addresses a new method for classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shape and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.47 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

scholarly journals 833 A scalable deep learning framework for rapid automated annotation of histologic and morphologic features from large unlabeled pan-cancer H&E datasets

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A874-A874
Author(s):  
David Soong ◽  
David Soong ◽  
David Soong ◽  
Anantharaman Muthuswamy ◽  
Clifton Drew ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Solid Tumor ◽  
Blood Cells ◽  
White Blood Cells ◽  
Digital Pathology ◽  
Area Under The Curve ◽  
Morphological Characteristics ◽  
Learning Framework ◽  
Tumor Types

BackgroundRecent advances in machine learning and digital pathology have enabled a variety of applications including predicting tumor grade and genetic subtypes, quantifying the tumor microenvironment (TME), and identifying prognostic morphological features from H&E whole slide images (WSI). These supervised deep learning models require large quantities of images manually annotated with cellular- and tissue-level details by pathologists, which limits scale and generalizability across cancer types and imaging platforms. Here we propose a semi-supervised deep learning framework that automatically annotates biologically relevant image content from hundreds of solid tumor WSI with minimal pathologist intervention, thus improving quality and speed of analytical workflows aimed at deriving clinically relevant features.MethodsThe dataset consisted of >200 H&E images across >10 solid tumor types (e.g. breast, lung, colorectal, cervical, and urothelial cancers) from advanced disease patients. WSI were first partitioned into small tiles of 128μm for feature extraction using a 50-layer convolutional neural network pre-trained on the ImageNet database. Dimensionality reduction and unsupervised clustering were applied to the resultant embeddings and image clusters were identified with enriched histological and morphological characteristics. A random subset of representative tiles (<0.5% of whole slide tissue areas) from these distinct image clusters was manually reviewed by pathologists and assigned to eight histological and morphological categories: tumor, stroma/connective tissue, necrotic cells, lymphocytes, red blood cells, white blood cells, normal tissue and glass/background. This dataset allowed the development of a multi-label deep neural network to segment morphologically distinct regions and detect/quantify histopathological features in WSI.ResultsAs representative image tiles within each image cluster were morphologically similar, expert pathologists were able to assign annotations to multiple images in parallel, effectively at 150 images/hour. Five-fold cross-validation showed average prediction accuracy of 0.93 [0.8–1.0] and area under the curve of 0.90 [0.8–1.0] over the eight image categories. As an extension of this classifier framework, all whole slide H&E images were segmented and composite lymphocyte, stromal, and necrotic content per patient tumor was derived and correlated with estimates by pathologists (p<0.05).ConclusionsA novel and scalable deep learning framework for annotating and learning H&E features from a large unlabeled WSI dataset across tumor types was developed. This automated approach accurately identified distinct histomorphological features, with significantly reduced labeling time and effort required for pathologists. Further, this classifier framework was extended to annotate regions enriched in lymphocytes, stromal, and necrotic cells – important TME contexture with clinical relevance for patient prognosis and treatment decisions.

scholarly journals Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

2019 ◽  
Vol 9 (2) ◽  
pp. 173
Author(s):  
Apri Nur Liyantoko ◽  
Ika Candradewi ◽  
Agus Harjoko
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Back Propagation ◽  
Cell Types ◽  
Diagnostic Process ◽  
The Subject ◽  
Processing Techniques

 Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and  multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

Segmentation of Leukemia Cells Using Clustering

2019 ◽  
Vol 10 (2) ◽  
pp. 39-48
Author(s):  
Eman Mostafa ◽  
Heba A. Tag El-Dien
Keyword(s):  
Bone Marrow ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Leukemia Cells ◽  
Microscopic Image ◽  
Processing Stage ◽  
Clustering Techniques ◽  
Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

Optimized Binary Bat algorithm for classification of white blood cells

Measurement ◽  
2019 ◽  
Vol 143 ◽  
pp. 180-190 ◽  
Author(s):  
Deepak Gupta ◽  
Jatin Arora ◽  
Utkarsh Agrawal ◽  
Ashish Khanna ◽  
Victor Hugo C. de Albuquerque
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Bat Algorithm

Automatic segmentation, counting, size determination and classification of white blood cells

Measurement ◽  
2014 ◽  
Vol 55 ◽  
pp. 58-65 ◽  
Author(s):  
Sedat Nazlibilek ◽  
Deniz Karacor ◽  
Tuncay Ercan ◽  
Murat Husnu Sazli ◽  
Osman Kalender ◽  
...  
Keyword(s):  
Blood Cells ◽  
Automatic Segmentation ◽  
White Blood Cells ◽  
Size Determination

ROBUST DISCRIMINATION OF LEUKOCYTES PROTUBERANT TYPES FOR EARLY DIAGNOSIS OF LEUKEMIA

2019 ◽  
Vol 19 (06) ◽  
pp. 1950055 ◽  
Author(s):  
ISRA NAZ ◽  
NAZEER MUHAMMAD ◽  
MUSSARAT YASMIN ◽  
MUHAMMAD SHARIF ◽  
JAMAL HUSSAIN SHAH ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Blood Cells ◽  
Detection System ◽  
White Blood Cells ◽  
Training Model ◽  
Blood Film ◽  
Morphological Examination ◽  
Efficient Detection ◽  
Laboratory Technology

Advanced laboratory technology has made blood testing more automated, robust and tests are being implemented comprehensively. Leukocytes type differentiation is a critical hematological images analysis step of blood film as it delivers valuable information in diagnosis of several diseases. At present, the morphological examination of leukocytes is done manually and this process is very tedious, inefficient and slow. Although many white blood cells detection or classification techniques are presented by different researchers, there is still a need of fully automated and an efficient detection system of blood cells with its particular types for an early diagnosis of leukemia. This paper presents a technique for the classification of protuberant types of leukocytes and early diagnosis of leukemia. The work is divided into the following main stages: (a) image augmentation, (b) wavelet composition and decomposition for attaining high and low frequency bands of the cell image, (c) convolutional neural network (CNN) training model for the classification of leukocytes categories and (d) prediction of leukemia. The main intention behind this study is to develop an automated, robust and efficient classification and detection system of leukocytes for microscopic blood images.

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