The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks

This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi

Automatic detection and classification of leukaemia cells

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.The Institute of Higher Education of Malaysia and the Universiti Sains Islam Malaysia (USIM)

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

A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells

Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person's blood sample under a microscope. They identify and categorize leukemia by counting various blood cells and morphological features. This technique is time-consuming for the prediction of leukemia. The pathologist's professional skills and experiences may be affecting this procedure, too. In computer vision, traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images such as microscopic blood cells. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells. First, we have divided the previous works into six categories based on the output of the models. Then, we describe various steps of detection and classification of acute leukemia and WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction), Classification, and focus on classification step in the methods. Finally, we divide automated detection and classification of acute leukemia and WBCs into three categories, including traditional, Deep Neural Network (DNN), and mixture (traditional and DNN) methods based on the type of classifier in the classification step and analyze them. The results of this study show that in the diagnosis and classification of acute leukemia and WBCs, the Support Vector Machine (SVM) classifier in traditional machine learning models and Convolutional Neural Network (CNN) classifier in deep learning models have widely employed. The performance metrics of the models that use these classifiers compared to the others model are higher

Fusion noise-removal technique with modified dark-contrast algorithm for robust segmentation of acute leukemia cell images

Segmentation is the major area of interest in the field of image processing stage. In an automatic diagnosis of acute leukemia disease, the crucial process is to achieve the accurate segmentation of acute leukemia blood image. Generally, there are three requirements of image segmentation for medical purposes, namely; accuracy, robustness and effectiveness which have received considerable critical attention. As such, we propose a new (modified) dark contrast enhancement technique to enhance and automatically segment the acute leukemic cells. Subsequently, we used a fusion 7 × 7 median filter as well as the seeded region growing area extraction (SRGAE) algorithm to minimise the salt-and-pepper noise, apart from preserving the post-segmentation edge. As per the outcomes, the accuracy, sensitivity, and specificity of this method were 91.02%, 83.68%, and 91.57% respectively

Optical mesoscopy, machine learning and computational microscopy enable high information content diagnostic imaging of blood films

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade-off between magnification and spatial resolution. To address this, we investigated three state-of-the-art approaches to microscopic imaging of blood films which leverage recent advances in optical and computational imaging and analysis to increase the information capture capacity of the optical microscope: optical mesoscopy, which uses a giant microscope objective (Mesolens) to enable high resolution imaging at low magnification; Fourier ptychographic microscopy, a computational imaging method which relies on oblique illumination with a series of LEDs to capture high resolution information; and deep neural networks which can be trained to increase the quality of low magnification, low resolution images. We compare and contrast the performance of these techniques for blood film imaging for the exemplar case of Giemsa-stained peripheral blood smears. Using computational image analysis and shape-based object classification we demonstrate their use for automated analysis of red blood cell morphology and visualization and detection of small blood borne parasites such as the malarial parasite Plasmodium falciparum. Our results demonstrate that these new methods greatly increase the information capturing capacity of the light microscope with transformative potential for haematology and more generally across digital pathology. This article is protected by copyright. All rights reserved