Leukaemia’s Cells Pattern Tracking Via Multi-phases Edge Detection Techniques

Edge detection involves identifying and tracing the sudden sharp discontinuities to extract meaningful information from an image. The purpose of this paper is to improve detecting the leukaemia edges in the blood cell image. Toward this end, two distinctive procedures are developed which are Ant Colony Optimization Algorithm and the gradient edge detectors (Sobel, Prewitt and Robert). The latter involves image filtering, binarization, kernel convolution filtering and image transformation. Meanwhile, ACO involves filtering, enhancement, detection and localisation of the edges. Finally, the performance of the edge detection methods ACO, Sobel, Prewitt and Robert is compared to determine the best edge detection method. The results revealed that the Prewitt edge detection method produced an optimal performance for detecting edges of leukaemia cells with a value of 107%. Meanwhile, the ACO, Sobel and Robert yielded performance results of 76%, 102% and 93% respectively. Overall findings indicated that the gradient edge detection methods are superior to the Ant Colony Optimization method

AVALIAÇÃO DE TÉCNICAS DE SEGMENTAÇÃO PARA LEUCÓCITOS EM IMAGENS DE SANGUE

A leucemia é um tipo de câncer que se origina na medula óssea e é caracterizado pela proliferação anormal de leucócitos no sangue. Para que ocorra a identificação correta dos linfoblastos, os especialistas examinam cada lâmina de sangue do paciente. Este método é influenciado por fatores como a experiência do hematologista e uma grande quantidade de trabalho por analisar imagem por imagem, isso pode resultar em relatórios não padronizados e até erros. Uma solução de baixo custo e eficiente é a utilização de sistema que examine as imagens microscópicas de sangue. Concluiu-se a partir da revisão literária que o processo de automação desse sistema depende de uma segmentação adequada. Neste trabalho, comparamos 9 métodos de segmentação em três bases de imagens públicas com o objetivo de verificar os erros nos métodos a fim de determinar qual deles apresenta os melhores resultados

Unsupervised segmentation technique for acute leukemia cells using clustering algorithms

Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year.There are two main categories for leukaemia, which are acute and chronic leukaemia.The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if the identification of acute leukaemia cells could be done fast and effectively, proper treatment and medicine could be delivered. Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image.In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. Then, median filter and seeded region growing area extraction algorithms have been applied, to smooth the region of segmented blast and to remove the large unwanted regions from the image, respectively.Comparisons among the three clustering algorithms are made in order to measure the performance of each clustering algorithm on segmenting the blast area. Based on the good sensitivity value that has been obtained, the results indicate that moving k-means clustering algorithm has successfully produced the fully segmented blast region in acute leukaemia image. Hence, indicating that the resultant images could be helpful to haematologists for further analysis of acute leukaemia

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm subimages are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method

Classification of acute lymphoblastic leukemia using deep learning

Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children’s bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diag- nosis of ALL with a computer-aided system, which yields accurate result by using image proces- sing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers Naïve Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists

Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward

Gradient Boosted Trees on Transcriptomics: Diagnosis of Acute Myeloid Leukaemia

Η οξεία μυελογενής λευχαιμία (ΟΜΛ) είναι ένας τύπος καρκίνου που εμφανίζεται κυρίως σε ενήλικες με περιστασιακή έλλειψη συμπτωμάτων. Χαρακτηρίζεται από τον πολλαπλασιασμό, τη μη φυσιολογική διαφοροποίηση και σπάνια από την ατελή διαφοροποίηση των αιμοποιητικών κυττάρων. Επηρεάζει 0.3 με 5.3 ανά 100000 άτομα παγκοσμίως κάθε χρόνο. Εάν ένας ασθενής με ΟΜΛ δε λάβει την κατάλληλη θεραπεία, η μέση επιβίωση είναι περίπου 2 μήνες. Η γρήγορη διάγνωση είναι πολύ σημαντική και μπορεί να ωφελήσει τη συνολική επιβίωση. Στην παρούσα διπλωματική εργασία, τεχνικές μηχανικής μάθησης εφαρμόζονται σε δεδομένα μεταγραφωματικής με σκοπό την ανάπτυξη ενός εργαλείου που θα προβλέπει εάν ένα άτομο είναι ασθενής ΟΜΛ ή είναι υγιής. Συγκεκριμένα, ένας σύγχρονος αλγόριθμος μηχανικής μάθησης που ανήκει στην κατηγορία των δέντρων ενισχυμένης μάθησης, ο CatBoost, εφαρμόζεται σε δεδομένα μικροσυστοιχιών γονιδιακής έκφρασης που αποτελούνται από 3374 άτομα, ασθενείς ΟΜΛ και υγιείς, και 34 probe sets ως χαρακτηριστικά (CatBoost34) και σε ένα υποσύνολο των 3374 ατόμων που αποτελείται από 2177 άτομα, την ηλικία τους και 26 probe sets ως χαρακτηριστικά (CatBoost26). Η απόδοση του CatBoost26 μοντέλου είναι η καλύτερη στη βιβλιογραφία αναφορικά με τη πρόβλεψη της ΟΜΛ χρησιμοποιώντας παρόμοια ή όχι δεδομένα.Acute myeloid leukaemia (AML) is a type of cancer which mostly occurs in adults with occasionally lack of symptoms. It is characterized by proliferative, abnormally differentiated and infrequently poorly differentiated hemopoietic cells. It affects 0.3 to 5.3 per 100000 people around the world each year. If the AML patients do not undergo treatment, the median survival is approximately 2 months. Its quick diagnosis is very important and can benefit the overall survival. In the current diploma thesis, machine learning techniques are applied on transcriptomics data in order to develop a new screening tool which could predict if an individual has AML or is healthy. More specifically, a state-of-the-art machine learning algorithm which belongs to the category of gradient boosted trees, CatBoost, is applied on gene expression microarray datasets that consist of 3374 individuals, AML patients and healthy subjects, and 34 probe sets as features (CatBoost34) and a subset of 3374 subjects which consists of 2177 individuals, their age and 26 probe sets as features (CatBoost26). The performance of CatBoost26 model is the best one in the literature as regards the prediction of AML using similar or not data

An intelligent decision support system for acute lymphoblastic leukaemia detection

The morphological analysis of blood smear slides by haematologists or haematopathologists is one of the diagnostic procedures available to evaluate the presence of acute leukaemia. This operation is a complex and costly process, and often lacks standardized accuracy owing to a variety of factors, including insufficient expertise and operator fatigue. This research proposes an intelligent decision support system for automatic detection of acute lymphoblastic leukaemia (ALL) using microscopic blood smear images to overcome the above barrier. The work has four main key stages. (1) Firstly, a modified marker-controlled watershed algorithm integrated with the morphological operations is proposed for the segmentation of the membrane of the lymphocyte and lymphoblast cell images. The aim of this stage is to isolate a lymphocyte/lymphoblast cell membrane from touching and overlapping of red blood cells, platelets and artefacts of the microscopic peripheral blood smear sub-images. (2) Secondly, a novel clustering algorithm with stimulating discriminant measure (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of the nucleus and cytoplasm of lymphocytic cell membranes. The SDM measures are used in conjunction with Genetic Algorithm for the clustering of nucleus, cytoplasm, and background regions. (3) Thirdly, a total of eighty features consisting of shape, texture, and colour information from the nucleus and cytoplasm of the identified lymphocyte/lymphoblast images are extracted. (4) Finally, the proposed feature optimisation algorithm, namely a variant of Bare-Bones Particle Swarm Optimisation (BBPSO), is presented to identify the most significant discriminative characteristics of the nucleus and cytoplasm segmented by the SDM-based clustering algorithm. The proposed BBPSO variant algorithm incorporates Cuckoo Search, Dragonfly Algorithm, BBPSO, and local and global random walk operations of uniform combination, and Lévy flights to diversify the search and mitigate the premature convergence problem of the conventional BBPSO. In addition, it also employs subswarm concepts, self-adaptive parameters, and convergence degree monitoring mechanisms to enable fast convergence. The optimal feature subsets identified by the proposed algorithm are subsequently used for ALL detection and classification. The proposed system achieves the highest classification accuracy of 96.04% and significantly outperforms related meta-heuristic search methods and related research for ALL detection