261 research outputs found

    Segmentation and classification of leukocytes using neural networks: a generalization direction

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    In image digital processing, as in other fields, it is commonly difficult to simultaneously achieve a generalizing system and a specialistic system. The segmentation and classification of leukocytes is an application where this fact is evident. First an exclusively supervised approach to segmentation and classification of blood white cells images is shown. As this method produces some drawbacks related to the specialistic/generalized problems, another process formed by two neural networks is proposed. One is an unsupervised network and the other one is a supervised neural network. The goal is to achieve a better generalizing system while still doing well the role of a specialistic system. We will compare the performance of the two approaches

    Deep learning a boon for biophotonics

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    This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

    An empirical study on ensemble of segmentation approaches

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    Riconoscere oggetti all’interno delle immagini richiede delle abilità complesse che richiedono una conoscenza del contesto e la capacità di identificare i bordi degli oggetti stessi. Nel campo della computer vision, questo compito è chiamato segmentazione semantica e riguarda la classificazione di ogni pixel all’interno di un’immagine. Tale compito è di primaria importanza in molti scenari reali: nei veicoli autonomi, dove permette l’identificazione degli oggetti che circondano il veicolo, o nella diagnosi medica, in cui migliora la capacità di identificare patologie pericolose e quindi mitigare il rischio di serie conseguenze. In questo studio, proponiamo un nuovo modello per un multiclassificatore in grado di risolvere il compito di segmentazione semantica. Il modello si basa su reti neurali convoluzionali (CNN) e transformers. Un multiclassificatore usa diversi modelli le cui stime vengono aggregate così da ottenere l’output del sistema di multiclassificazione. Le prestazioni e la qualità delle previsioni dell’ensemble sono fortemente connessi ad alcuni fattori, tra cui il più importante è la diversità tra i singoli modelli. Nell’approccio qui proposto, abbiamo ottenuto questo risultato adottando diverse loss functions e testando con diversi metodi di data augmentation. Abbiamo sviluppato questo metodo combinando DeepLabV3+, HarDNet-MSEG e dei Pyramid Vision Transformers (PVT). La soluzione qui sviluppata è stata poi esaminata mediante un’ampia valutazione empirica in 5 diversi scenari: rilevamento di polipi, rilevamento della pelle, riconoscimento di leucociti, rilevamento di microorganismi e riconoscimento di farfalle. Il modello fornisce dei risultati che sono allo stato dell’arte. Tutte le risorse sono disponibili online all’indirizzo https://github.com/AlbertoFormaggio1/Ensemble-Of-Segmentation.Recognizing objects in images requires complex skills that involve knowledge about the context and the ability to identify the borders of the objects. In computer vision, this task is called semantic segmentation and it pertains to the classification of each pixel in an image. The task is of main importance in many real-life scenarios: in autonomous vehicles, it allows the identification of objects surrounding the vehicle; in medical diagnosis, it improves the ability of early detecting dangerous pathologies and thus to mitigate the risk of serious consequences. In this work, we propose a new ensemble method able to solve the semantic segmentation task. The model is based on convolutional neural networks (CNNs) and transformers. An ensemble uses many different models whose predictions are aggregated to form the output of the ensemble system. The performance and quality of the ensemble prediction are strongly connected with some factors, one of the most important is the diversity among individual models. In our approach, this is enforced by adopting different loss functions and testing different data augmentation. We developed the proposed method by combining DeepLabV3+, HarDNet-MSEG, and Pyramid Vision Transformers. The developed solution was then assessed through an extensive empirical evaluation in five different scenarios: polyp detection, skin detection, leukocytes recognition, environmental microorganism detection, and butterfly recognition. The model provides state-of-the-art results. All resources will be available online at https://github.com/AlbertoFormaggio1/Ensemble-Of-Segmentation

    USING CONVOLUTIONAL NEURAL NETWORKS FOR FINE GRAINED IMAGECLASSIFICATION OF ACUTE LYMPHOBLASTIC LEUKEMIA

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    Acute lymphoblastic leukemia (ALL) is a cancer of bone marrow stems cells that results in the overproduction of lymphoblasts. ALL is diagnosed through a series of tests which includes the minimally invasive microscopic examination of a stained peripheral blood smear. During examination, lymphocytes and other white blood cells (WBCs) are distinguished from abnormal lymphoblasts through fine-grained distinctions in morphology. Manual microscopy is a slow process with variable accuracy that depends on the laboratorian\u27s skill level. Thus automating microscopy is a goal in cell biology. Current methods involve hand-selecting features from cell images for input to a variety of standard machine learning classi ers. Underrepresented in WBC classi cation, yet successful in practice, is the convolutional neural network (CNN) that learns features from whole image input. Recently, CNNs are contending with humans in large scale and ne-grained image classi cation of common objects. In light of their e ectiveness, CNNs should be a consideration in cell biology. This work compares the performance of a CNN with standard classi ers to determine the validity of using whole cell images rather than hand-selected features for ALL classification

    Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle

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    Muscle regeneration process tracking and analysis aim to monitor the injured muscle tissue section over time and analyze the muscle healing procedure. In this procedure, as one of the most diverse cell types observed, white blood cells (WBCs) exhibit dynamic cellular response and undergo multiple protein expression changes. The characteristics, amount, location, and distribution compose the action of cells which may change over time. Their actions and relationships over the whole healing procedure can be analyzed by processing the microscopic images taken at different time points after injury. The previous studies of muscle regeneration usually employ manual approach or basic intensity process to detect and count WBCs. In comparison, computer vision method is more promising in accuracy, processing speed, and labor cost. Besides, it can extract features like cell/cluster size and eccentricity fast and accurately. In this thesis, we propose an automated quantifying and analysis framework to analyze the WBC in light microscope images of uninjured and injured skeletal muscles. The proposed framework features a hybrid image segmentation method combining the Localized Iterative Otsu’s threshold method assisted by neural networks classifiers and muscle edge detection. In specific, both neural network and convoluted neural network based classifiers are studied and compared. Via this framework, the CD68-positive WBC and 7/4-positive WBC quantification and density distribution results are analyzed for demonstrating the effectiveness of the proposed method

    Red blood cell segmentation and classification method using MATLAB

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    Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very important process for early detection of related disease such as malaria and anemia before suitable follow up treatment can be proceed. Some of the human disease can be showed by counting the number of red blood cells. Red blood cell count gives the vital information that help diagnosis many of the patient’s sickness. Conventional method under blood smears RBC diagnosis is applying light microscope conducted by pathologist. This method is time-consuming and laborious. In this project an automated RBC counting is proposed to speed up the time consumption and to reduce the potential of the wrongly identified RBC. Initially the RBC goes for image pre-processing which involved global thresholding. Then it continues with RBCs counting by using two different algorithms which are the watershed segmentation based on distance transform, and the second one is the artificial neural network (ANN) classification with fitting application depend on regression method. Before applying ANN classification there are step needed to get feature extraction data that are the data extraction using moment invariant. There are still weaknesses and constraints due to the image itself such as color similarity, weak edge boundary, overlapping condition, and image quality. Thus, more study must be done to handle those matters to produce strong analysis approach for medical diagnosis purpose. This project build a better solution and help to improve the current methods so that it can be more capable, robust, and effective whenever any sample of blood cell is analyzed. At the end of this project it conducted comparison between 20 images of blood samples taken from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method has been tested on blood cell images and the effectiveness and reliability of each of the counting method has been demonstrated
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