Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding

The paper proposes a robust approach to automatic segmentation of leukocyte‟s nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert haematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm

이중 합성곱 신경망을 이용한 백혈구 백분율 자동 분석 시스템에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 공과대학 협동과정 바이오엔지니어링전공, 2017. 8. 김희찬.Leukocyte or white blood cell differential count is an essential examination modality of hematology laboratory in diagnosis of various blood disorders. However, it requires highly experienced hematologists for correct diagnosis from samples with inter- and intra-sample variations. Due to tedious, time and cost consuming procedure of manual differential count, there has been high demands for development of automated system. In order for it to be applicable in clinical hematology laboratories, an automated system will have to detect and classify leukocytes of different maturation stages, especially in bone marrow aspirate smears. This has been a challenging problem in computer vision, image processing, and machine learning, because of complex nature of bone marrow aspirate smear. The leukocyte has multiple maturation stages, and these maturation stages have small inter-class differences, so it is difficult to differentiate even with expert knowledge. Moreover, a problem of color, shape, and size variations among samples exists and a problem of touching cell due to high leukocyte density of bone marrow aspirate smear exists. In this dissertation, an automated leukocyte differential count system for bone marrow aspirate smear was developed to overcome problems of manual differential count and to fulfill clinically unmet needs. The system should perform the differential count with high accuracy and objectivity, and high throughput and efficiency. Moreover, it should overcome challenges of bone marrow aspirate smear. To this end, a large dataset of bone marrow smear was collected for development of a detection and a classification algorithms. Watershed transformation and saliency map were utilized for single-leukocyte detection, and the dual-stage convolutional neural network that learns global and local features of complex leukocyte maturation stages was proposed for classification. Lastly, a probability guidance algorithm was proposed for integration of detection and classification algorithms. The performance of proposed system was assessed with ten leukocyte maturation stages of myeloid and erythroid series in bone marrow aspirate smears. Total of 200 large (1388 × 1040) digital images of bone marrow aspirate smears and 2,323 small (96 × 96) single leukocyte digital images were collected. The proposed system showed a state-of-the-art performance. It achieved an average detection accuracy of 96.09% and an average classification accuracy of 97.06%, and it was able to differential count 100 leukocytes in 4 to 5 seconds. This proposes a new paradigm in diagnosis of blood disorder and showed a potential of deep learning, especially the convolutional neural network, in medical image processing. The proposed system is expected to increase the total number of analyzed leukocytes in a sample, which will provide more statistically reliable information of a patient for diagnosis.Chapter 1 Introduction 1 1.1. Introduction to Hematology 2 1.2. Introduction to Convolutional Neural Network 12 1.3. Thesis Objectives 16 Chapter 2 Leukocyte Data Collection 19 2.1. Sample Preparation and Acquisition 20 2.2. Dataset Collection and Preparation 23 Chapter 3 Leukocyte Classification 27 3.1. Introduction 28 3.2. Methods 36 3.2.1. Data Collection and Preparation 36 3.2.2. Data Oversampling and Augmentation 38 3.2.3. Convolutional Neural Network Architecture and Dual-stage Convolutional Neural Network 40 3.2.4. Convolutional Neural Network Training 43 3.2.5. Implementation 46 3.2.6. Evaluation Metrics 46 3.3. Results and Discussion 48 3.4. Conclusion 66 Chapter 4 Implementation of Automated Leukocyte Differential Count System 67 4.1. System Overview 68 4.2. Leukocyte Detection 70 4.2.1. Introduction 70 4.2.2. Detection Algorithm 75 4.2.3. Experimental Setup and Evaluation 81 4.2.4. Results and Discussion 82 4.3. Automated Leukocyte Differential Count System 92 4.3.1. Implementation of Detection and Classification Algorithms 92 4.3.2. Graphical User Interface Design 93 4.3.3. Probability Guidance Algorithm 95 4.3.4. Experimental Setup and Evaluation 97 4.3.5. Results and Discussion 98 4.4. Conclusion 102 Chapter 5 Thesis Summary and Future Work 104 5.1 Thesis Summary and Contributions 105 5.2 Future Work 109 Bibliography 115 Abstract in Korean 122Docto

Manual and automatic image analysis segmentation methods for blood flow studies in microchannels

In blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. This work shows different ways to process the images which contain various blood phenomena happening in microfluidic devices and in microcirculation. For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels will be presented. Most of the past blood flow experimental data have been collected and analyzed by means of manual methods, that can be extremely reliable, but they are highly time-consuming, user-intensive, repetitive, and the results can be subjective to user-induced errors. For this reason, it is crucial to develop image analysis methods able to obtain the data automatically. Concerning automatic image analysis methods for individual RBCs tracking and to measure the well known microfluidic phenomena cell-free layer, two developed methods are presented and discussed in order to demonstrate their feasibility to obtain accurate data acquisition in such studies. Additionally, a comparison analysis between manual and automatic methods was performed.This project has been funded by Portuguese national funds of FCT/MCTES (PIDDAC) through the base funding from the following research units: UIDB/00532/2020 (Transport Phenomena Research Center—CEFT), UIDB/04077/2020 (Mechanical Engineering and Resource Sustainability Center—MEtRICs), UIDB/00690/2020 (CIMO). The authors are also grateful for the partial funding of FCT through the projects, NORTE-01-0145-FEDER-029394 (PTDC/EMD-EMD/29394/2017) and NORTE-01-0145-FEDER-030171 (PTDC/EMD-EMD/30171/2017) funded by COMPETE2020, NORTE2020, PORTUGAL2020 and FEDER. D. Bento acknowledges the PhD scholarship SFRH/BD/ 91192/2012 granted by FCT

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Automated image analysis of lung branching morphogenesis from microscopic images of fetal rat explants

Article ID 820214Background. Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour, and peripheral airway buds of lung explants during cellular development from microscopic images. Methods. The outer contour was defined using an adaptive and multiscale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelium was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds was counted as the skeleton branched ends from a skeletonized image of the lung inner epithelia. Results. The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Nonsignificant differences were found between the automatic and manual results in all culture days. Conclusions. The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lighting characteristics and allowing a reliable comparison between different researchers.The authors acknowledge Foundation for Science and Technology (FCT), Portugal, for the fellowship with the references: SFRH/BD/74276/2010 and SFRH/BPD/46851/2008

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