An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic Threshold

Madhloom, Hayan T.; Kareem, Sameem Abdul; Ariffin, Hany; Zaidan, A. A.; Alanazi, H.; Zaidan, B. B.

doi:10.3923/jas.2010.959.966

Cited by 126 publications

(71 citation statements)

References 11 publications

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“…To cope with the enormous amount of bio image data, the computer based image analysis is required to improve the working efficiency and to decrease inter-reader variability. Therefore, quite a lot of software and applications have been developed for different tasks like cell detection [2], [3], bio-image segmentation [4], [5] and cell phenotype classification [6], [7]. Recently, the collaboration between biomedical researchers and computer engineers has pushed the development of the area of Bioimage informatics forward and developed many useful applications.…”

Section: Introdutionmentioning

confidence: 99%

Histology Image Classification Using Supervised Classification and Multimodal Fusion

Meng

Lin

Shyu

et al. 2010

2010 IEEE International Symposium on Multimedia

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Abstract-The fast development of microscopy imaging techniques nowadays promotes the generation of a large amount of data. These data are very crucial not only for theoretical biomedical research but also for clinical usage. In order to decrease the inter-intra observer variability and save the human effort on labeling and classifying these images, a lot of research efforts have been devoted to the development of algorithms for biomedical images. Among such efforts, histology image classification is one of the most important areas due to its broad applications in pathological diagnosis such as cancer diagnosis. To improve classification accuracy, most of the previous work focuses on extracting more features and building algorithms for a specific task. This paper proposes a framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification. In the proposed framework, a cell image is first divided into 25 blocks to reduce the spatial complexity of computation, and one C-RSPM model is built on each block set which contains blocks in the same location from different images. For each testing image, our proposed framework first classifies each of its blocks using the C-RSPM classification model built for that block set, and then applies a multimodal late fusion algorithm with a weighted majority voting strategy to decide the final class label of the whole image. Experimenting using three-fold cross validation with three benchmark histology data sets shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.

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Section: Introdutionmentioning

confidence: 99%

Histology Image Classification Using Supervised Classification and Multimodal Fusion

Meng

Lin

Shyu

et al. 2010

2010 IEEE International Symposium on Multimedia

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“…Although there has been a large amount of work done in the area of WBC segmentation, many of the previous researchers did not focus on localization and extracting the cell of interest from the original image [6,[8][9][10][13][14][15]. In cases where there were many cells of interest in the same image, the focus has always been about the segmentation of WBC or blast directly and extracting the cell of interest sub image manually as done by Sadeghian, et al [6].…”

Section: Resultsmentioning

confidence: 99%

“…In an earlier work done by the present author [13], a method based on contrast stretching and image arithmetic to localize the WBC nucleus was reported. The method managed to obtain up to 95% accuracy in localization and segmentation the WBC's.…”

Section: Literature Reviewmentioning

confidence: 95%

An Image Processing Application for the Localization and Segmentation of Lymphoblast Cell Using Peripheral Blood Images

2011

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An important preliminary step in the diagnosis of leukemia is the visual examination of the patient's peripheral blood smear under the microscope. Morphological changes in the white blood cells can be an indicator of the nature and severity of the disease. Manual techniques are labor intensive, slow, error prone and costly. A computerized system can be used as a supportive tool for the specialist in order to enhance and accelerate the morphological analysis process. This research present a new method that integrates color features with the morphological reconstruction to localize and isolate lymphoblast cells from a microscope image that contains many cells. The localization and segmentation are conducted using a proposed method that consists of an integration of several digital image processing techniques. 180 microscopic blood images were tested, and the proposed framework managed to obtain 100% accuracy for the localization of the lymphoblast cells and separate it from the image scene. The results obtained indicate that the proposed method can be safely used for the purpose of lymphoblast cells localization and segmentation and subsequently, aiding the diagnosis of leukemia.

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“…Red Blood Cells (RBC) and platelets. The algorithm for pre-processing and segmentation of lymphocytes is as follows [4]:…”

Section: Image Preprocessingmentioning

confidence: 99%

Leukemia Prediction using Random Forest Algorithm

Naik¹,

Tjprc²

2018

IJCSEITR

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An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic Threshold

Cited by 126 publications

References 11 publications

Histology Image Classification Using Supervised Classification and Multimodal Fusion

Histology Image Classification Using Supervised Classification and Multimodal Fusion

An Image Processing Application for the Localization and Segmentation of Lymphoblast Cell Using Peripheral Blood Images

Leukemia Prediction using Random Forest Algorithm

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