Automatic Leukemia Cell Counting using Iterative Distance Transform for Convex Sets

The calculation of white blood cells on the acute leukemia microscopic images is one of the stages in the diagnosis of Leukemia disease. The main constraint on calculating the number of white blood cells is the precision in the area of overlapping white blood cells. The research on the calculation of the number of white blood cells overlapping generally based on geometry. However, there was still a calculation error due to over segment or under segment. This paper proposed an Iterative Distance Transform for Convex Sets (IDTCS) method to determine the markers and calculate the number of overlapping white blood cells. Determination of marker was performed on every cell both in single and overlapping white blood cell area. In this study, there were tree stages: segmentation of white blood cells, marker detection and white blood cell count, and contour estimation of every white blood cell. The used data testing was microscopic acute leukemia image data of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). Based on the test results, Iterative Distance Transform for Convex Sets IDTCS method performs better than Distance Transform (DT) and Ultimate Erosion for Convex Sets (UECS) method

Automated Detection of Acute Leukemia using K-mean Clustering Algorithm

Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze microscopic samples to make diagnostic assessments on the basis of characteristic cell features. Recently, computerized methods for cancer detection have been explored towards minimizing human intervention and providing accurate clinical information. This paper presents an algorithm for automated image based acute leukemia detection systems. The method implemented uses basic enhancement, morphology, filtering and segmenting technique to extract region of interest using k-means clustering algorithm. The proposed algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor (KNN) and Naive Bayes Classifier on the data-set of 60 samples.Comment: Presented in ICCCCS 201

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.

A Computer-Aided Method to Expedite the Evaluation of Prognosis for Childhood Acute Lymphoblastic Leukemia

This study presented a fully-automated computer-aided method (scheme) to detect metaphase chromosomes depicted on microscopic digital images, count the total number of chromosomes in each metaphase cell, compute the DNA index, and correlate the results to the prognosis of childhood acute lymphoblastic leukemia (ALL). The computer scheme first uses image filtering, threshold, and labeling algorithms to segment and count the number of the suspicious “chromosome,” and then computes a feature vector for each “detected chromosome.” Based on these features, a knowledge-based classifier is used to eliminate those “non-chromosome” objects (i.e., inter-phase cells, stain debris, and other kinds of background noises). Due to the possible overlap of the chromosomes, a classification criterion was used to identify the overlapped chromosomes and adjust the initially counted number of the total chromosomes in each image. In this preliminary study with 60 testing images (depicting metaphase chromosome cells) acquired from three pediatric patients, the computer scheme generated results matched with the diagnostic results provided by the clinical cytogeneticists. The results demonstrated the feasibility or potential of using a computerized method to replace the tedious and the reader-dependent diagnostic methods commonly used in genetic laboratories to date.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Automated cell counting system for chronic leukemia

Leukemia is a group of cancers which create a large amount of immature white blood cells. Abnormal numbers of white blood cells may suggest a screening of leukemia, and the blood sample is examined under the microscope to observe if the cells appear abnormal. The manual screening of chronic leukemia is time consuming and tedious while the Automated Hematology Analyzer is too expensive, particularly for the third world countries. This has been made exacerbated by the gold standard of biopsy inspiration which is painful and invasive for the patient. An automated cell counting (ACC) system for chronic leukemia has been developed to support and ease the routine of hematologist and technologist in the screening process and to give a quick and accurate result. The fusion of image processing technique has been proposed, which include four main stages, i.e. image acquisition, image segmentation, noise removal and counting process. Based on the sensitivity test over 100 images of chronic cells, an overall result shows 98.94% sensitivity of the system performance and the processing time recorded is less than 6 second per image. This proved an excellent level of ACC system performance. It is concluded that the system is suitable to be used as an automated counting system for chronic leukemia disease due to its sensitivity and ability to reduce the time taken for screening process

Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning

Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)).  As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier

Hematological image analysis for acute lymphoblastic leukemia detection and classification

Microscopic analysis of peripheral blood smear is a critical step in detection of leukemia.However, this type of light microscopic assessment is time consuming, inherently subjective, and is governed by hematopathologists clinical acumen and experience. To circumvent such problems, an efficient computer aided methodology for quantitative analysis of peripheral blood samples is required to be developed. In this thesis, efforts are therefore made to devise methodologies for automated detection and subclassification of Acute Lymphoblastic Leukemia (ALL) using image processing and machine learning methods.Choice of appropriate segmentation scheme plays a vital role in the automated disease recognition process. Accordingly to segment the normal mature lymphocyte and malignant lymphoblast images into constituent morphological regions novel schemes have been proposed. In order to make the proposed schemes viable from a practical and real–time stand point, the segmentation problem is addressed in both supervised and unsupervised framework. These proposed methods are based on neural network,feature space clustering, and Markov random field modeling, where the segmentation problem is formulated as pixel classification, pixel clustering, and pixel labeling problem respectively. A comprehensive validation analysis is presented to evaluate the performance of four proposed lymphocyte image segmentation schemes against manual segmentation results provided by a panel of hematopathologists. It is observed that morphological components of normal and malignant lymphocytes differ significantly. To automatically recognize lymphoblasts and detect ALL in peripheral blood samples, an efficient methodology is proposed.Morphological, textural and color features are extracted from the segmented nucleus and cytoplasm regions of the lymphocyte images. An ensemble of classifiers represented as EOC3 comprising of three classifiers shows highest classification accuracy of 94.73% in comparison to individual members. The subclassification of ALL based on French–American–British (FAB) and World Health Organization (WHO) criteria is essential for prognosis and treatment planning. Accordingly two independent methodologies are proposed for automated classification of malignant lymphocyte (lymphoblast) images based on morphology and phenotype. These methods include lymphoblast image segmentation, nucleus and cytoplasm feature extraction, and efficient classification

A cascaded classification-segmentation reversible system for computer-aided detection and cells counting in microscopic peripheral blood smear basophils and eosinophils images

Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process

HIGH-THROUGHPUT FLUORESCENCE MICROSCOPY FOR AUTOMATED CLINICAL APPLICATIONS

Fluorescence in situ hybridization (FISH) is a powerful tool for visualizing and detecting genetic abnormalities. Manual scoring FISH analysis is a tedious and labor-and-time-consuming task. Automated image acquisition and analysis provide an opportunity to overcome the difficulties. However, conventional fluorescence microscopes, the mostly used instrument for FISH imaging, have deficiencies. A multi-spectral image modality must be employed in order to visualize fluorescently dyed FISH probes for analysis, and the existing technologies are either two expensive, too slow, or both. Aiming at upgrading the current employed cytogenetic instrumentation, we developed a new imaging technique capable of simultaneously imaging multiple color spectra. Using the principle, we implemented a prototype system and conduct various characterization experiments. Experiment results (<1% peripheral geometric distortion, consistent signal response linearity, and ~2000 lp/mm spatial resolution) show no significant compromise in terms of optical performance. A detector alignment scheme was developed and performed to minimize registration error. The system has significantly faster acquisition speed than conventional fluorescence microscopes albeit the extra cost is quite insignificant

Sistem Klasifikasi Leukemia berdasarkan Citra Peripheral Blood Microscopic menggunakan Extreme Learning Machine

Leukemia merupakan salah satu kanker yang mematikan yang menyerang manusia di segala usia. Sebuah database SEER Incidence Database menyebutkan bahwa pada tahun 2019 teedapat kasus baru leukemia sebanyak 61.780 kasus dan 22.840 jiwa meninggal dunia akibat leukemia. Leukemia dikatakan sangat berbahaya karena penyakit ini merupakan jenis tumor cair sehingga bentuknya tidak dapat dilihat secara fisik. Namun perkembangan penyakit leukemia dapat diketahui dengan menghitung jumlah sel-sel darah yang terdapat dalam tubuh melalui tes mikroskopik. Hasil tes mikroskopik dapat diproses menggunakan bantuan machine learning untuk melakukan sistem klasifikasi. Metode klasifikasi yang sering digunakan dalam beberapa tahun terakhir adalah extreme learning machine(ELM). Extreme Learning Machine (ELM) memiliki istilah lain yang disebut dengan Single Hidden Layer Feedforward Neural Network (SLFNs), yaitu jaringan saraf tiruan feedforward dengan satu hidden layer. ELM mampu mengatasi permasalahan yang sering terjadi pada backpropagation. Dalam proses pembelajarannya, ELM memanfaatkan teori invers matriks Moore Penrose Pseudoinverse yang memiliki hasil generalisasi terbaik dengan waktu komputasi yang cepat. Pada penelitain ini, dilakukan klasifikasi leukemia berdasarkan citra peripheral blood microscopicmenggunakan Extreme Learning Machine (ELM). Tahapan-tahapan klasifikasi terdiri dari preprocessing mmenggunakan histogram equalization dan median filter bertujuan untuk perbaikan kualitas citra, ekstraksi fitur menggunakan gray level run length matrix digunakan untuk mengambil ciri statistik yang terdapat dalam citra dan klasifikasi citra leukemia menggunakan extreme learning machine.Hasil klasifikasi leukemia berdasarkan beberapa orientasi arah dengan tiga belas percobaan jumlah node pada hidden layer diperoleh hasil terbaik yaitu akurasi sebesar 100%, presisi sebesar 100% dan recall sebesar 100% pada orientasi 00 dengan 10 node hidden layer, orientasi 450 dengan 12 node hidden layer, orientasi 900 dengan 14 node hidden layer, dan orientasi 1350 dengan 10 node hidden layer