Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-1

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique

A Review on Classification of White Blood Cells Using Machine Learning Models

The machine learning (ML) and deep learning (DL) models contribute to exceptional medical image analysis improvement. The models enhance the prediction and improve the accuracy by prediction and classification. It helps the hematologist to diagnose the blood cancer and brain tumor based on calculations and facts. This review focuses on an in-depth analysis of modern techniques applied in the domain of medical image analysis of white blood cell classification. For this review, the methodologies are discussed that have used blood smear images, magnetic resonance imaging (MRI), X-rays, and similar medical imaging domains. The main impact of this review is to present a detailed analysis of machine learning techniques applied for the classification of white blood cells (WBCs). This analysis provides valuable insight, such as the most widely used techniques and best-performing white blood cell classification methods. It was found that in recent decades researchers have been using ML and DL for white blood cell classification, but there are still some challenges. 1) Availability of the dataset is the main challenge, and it could be resolved using data augmentation techniques. 2) Medical training of researchers is recommended to help them understand the structure of white blood cells and select appropriate classification models. 3) Advanced DL networks such as Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN can also be used in future techniques.Comment: 23 page

The watershed transform in pathological image analysis: application in rectiulocyte count from supravital stained smears

Background: Morphometric studies based on image analysis are a useful adjunct for quantitative analysis of microscopic images. However, effective separation of overlapping objects if often the bottleneck in image analysis techniques. We employ the watershed transform for counting reticulocytes from images of supravitally stained smears.Methods: The algorithm was developed with the Python programming platform, using the Numpy, Scipy and OpenCV libraries. The initial development and testing of the software were carried out with images from the American Society of Hematology Image Library. Then a pilot study with 30 samples was then taken up. The samples were incubated with supravital stain immediately after collection, and smears prepared. The smears were microphotographed at 100X objective, with no more than 150 RBCs per field. Reticulocyte count was carried out manually as well as by image analysis.Results: 600 out of 663 reticulocytes (90.49%) were correctly identified, with a specificity of 98%. The major difficulty faced was the slight bluish tinge seen in polychromatic RBCs, which were inconsistently detected by the software.Conclusions: The watershed transform can be used successfully to separate overlapping objects usually encountered in pathological smears. The algorithm has the potential to develop into a generalized cell classifier for cytopathology and hematology

Automatic detection for acute lymphoid leukemia images based non local region approach

Orientador: Prof. Dr. Lucas Ferrari de OliveiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 03/09/2018Inclui referências: p.90-95Resumo: A leucemia linfoide aguda (LLA) é o tipo de câncer mais comum a se manifestar na infância, apesar de apresentar rápida evolução em seu quadro clínico a LLA possui relativamente baixa mortalidade quando identificada e tratada em seu estágio inicial. Como o diagnóstico de LLA é feito por médicos hematologistas com base na análise microscópica de lâminas contendo amostras de sangue periférico, o que pode ser considerado um trabalho lento e cansativo impactando no desempenho do médico, o desenvolvimento de ferramentas que auxiliem neste processo é uma necessidade real. A proposta deste trabalho é apresentar um algoritmo capaz de segmentar os leucócitos existentes, extrair e selecionar características gerando uma representação compacta e por fim utilizar um classificador para diferenciar imagens de sangue periférico de pacientes saudáveis de pacientes portadores de LLA. A base de imagens ALL_IDB foi escolhida para ser utilizada por ser uma base de domínio público e também utilizada em outros trabalhos permitindo comparações precisas, e apresentar diversas dificuldades encontradas no trabalho com imagens provenientes de microscópio, como diferentes tipos de iluminação e zoom. Das 108 imagens utilizadas nos testes 107 foram classificadas corretamente, resultando em uma acurácia de 0,99 sendo este valor maior que o melhor trabalho encontrado na literatura atual, mesmo o único caso classificado erroneamente foi um falso positivo o que no contexto da aplicação é menos grave do que um falso negativo. Palavras-chave:Leucemia Linfoide Aguda,Processamento Imagens,Reconhecimento?? de??Padrões,Texturas,Classificadores Lineares.Abstract: Acute lymphoblastic leukemia (ALL) is the most common type of cancer in childhood. Despite presenting a rapid evolution in its clinical condition, ALL has a relatively low mortality when identified and treated in its initial stage. Due to the fact that the ALL diagnosis is made by hematologists based on the microscopic analysis of the peripheral blood smear slices, which can be considered a tedious and tiring work, impacting on the doctor's performance, the development of tools that would help in this process is a real necessity. Thus, the purpose of this work is to present an algorithm capable of segmenting the existing leukocytes from blood smear images, extracting and selecting the most representative features, generating a compact representation, so as to finally use a classifier to differentiate the peripheral blood smear images of healthy patients from patients with ALL. The ALL_IDB image base was chosen for being a public domain base and also used in other works, thus allowing accurate comparisons, as well as revealing several difficulties that are faced when working with microscopic images, such as different types of lighting and distinct zoom levels. The final results were expressive and reached an accuracy of 0.99, where, from the 108 images used in the tests, 107 were classified correctly. This result is higher than the best one found in the latest literature, and the only image classified as being wrong was a false positive which in the application context is not the worse case scenario. Keywords: Acute Lymphoid Leukemia, Image??Processing, Pattern??Recognition,Textures, Linear Classifiers

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

Review on Photomicrography based Full Blood Count (FBC) Testing and Recent Advancements

With advancements in related sub-fields, research on photomicrography in life science is emerging and this is a review on its application towards human full blood count testing which is a primary test in medical practices. For a prolonged period of time, analysis of blood samples is the basis for bio medical observations of living creatures. Cell size, shape, constituents, count, ratios are few of the features identified using DIP based analysis and these features provide an overview of the state of human body which is important in identifying present medical conditions and indicating possible future complications. In addition, functionality of the immune system is observed using results of blood tests. In FBC tests, identification of different blood cell types and counting the number of cells of each type is required to obtain results. Literature discuss various techniques and methods and this article presents an insightful review on human blood cell morphology, photomicrography, digital image processing of photomicrographs, feature extraction and classification, and recent advances. Integration of emerging technologies such as microfluidics, micro-electromechanical systems, and artificial intelligence based image processing algorithms and classifiers with cell sensing have enabled exploration of novel research directions in blood testing applications.