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

Madhloom, Hayan T.; Kareem, Sameem Abdul; Ariffin, Hany

doi:10.1007/s10916-011-9679-0

Cited by 56 publications

(27 citation statements)

References 10 publications

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“…image subtraction and image addition) is used for localization and segmentation of lymphoblast cells from peripheral blood smear images. [16] It gives accuracy of 90-95% in restoring the lymphoblast pixels from the original image. This is due to the color inconsistency of the lymphoblast cells.…”

Section: Peripheral Blood Smear Microscopic Image Segmentationmentioning

confidence: 99%

Peripheral blood smear image analysis: A comprehensive review

Mohammed

Mohamed

Far

et al. 2014

Journal of Pathology Informatics

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Peripheral blood smear image examination is a part of the routine work of every laboratory. The manual examination of these images is tedious, time-consuming and suffers from interobserver variation. This has motivated researchers to develop different algorithms and methods to automate peripheral blood smear image analysis. Image analysis itself consists of a sequence of steps consisting of image segmentation, features extraction and selection and pattern classification. The image segmentation step addresses the problem of extraction of the object or region of interest from the complicated peripheral blood smear image. Support vector machine (SVM) and artificial neural networks (ANNs) are two common approaches to image segmentation. Features extraction and selection aims to derive descriptive characteristics of the extracted object, which are similar within the same object class and different between different objects. This will facilitate the last step of the image analysis process: pattern classification. The goal of pattern classification is to assign a class to the selected features from a group of known classes. There are two types of classifier learning algorithms: supervised and unsupervised. Supervised learning algorithms predict the class of the object under test using training data of known classes. The training data have a predefined label for every class and the learning algorithm can utilize this data to predict the class of a test object. Unsupervised learning algorithms use unlabeled training data and divide them into groups using similarity measurements. Unsupervised learning algorithms predict the group to which a new test object belong to, based on the training data without giving an explicit class to that object. ANN, SVM, decision tree and K-nearest neighbor are possible approaches to classification algorithms. Increased discrimination may be obtained by combining several classifiers together.

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Section: Peripheral Blood Smear Microscopic Image Segmentationmentioning

confidence: 99%

Peripheral blood smear image analysis: A comprehensive review

Mohammed

Mohamed

Far

et al. 2014

Journal of Pathology Informatics

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show abstract

“…As in general-purpose images, there is no universal method or algorithm for segmenting medical images (Sharma and Aggarwal, 2010). The use of manual methods (Madhloom et al, 2012) in medical image analysis brings with many drawbacks like increasing time cost (Mohamed and Far, 2012;Nazlibilek et al, 2014) and leading to calculation errors. In recent years, various studies have been carried out by a significant number of scientists to remove these problems.…”

Section: Introductionmentioning

confidence: 99%

“…These studies are generally based on White Blood Cells (WBC) or Red Blood Cell (RBC) segmentation (Dey et al, 2015). Madhloom et al (2012) have developed a method of segmenting lymphoblast cells with high accuracy using microscopic blood images. They have combined morphological reconstruction with the color properties of the cell to differentiate lymphoblast cells from other blood cells.…”

Section: Introductionmentioning

confidence: 99%

A new approach for fully automated segmentation of peripheral blood smears

Elen

Turan

2018

Int. j. adv. appl. sci.

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Peripheral blood smear is microscopically examining technique for blood samples from patients by painting special dyes in clinic laboratories. Blood diseases can be diagnosed by examining morphology, numbers and percentages of leukocyte, erythrocyte and thrombocyte cells in blood samples. However, this method is a considerably time-consuming process and requires an evaluation performed by a hematology specialist. It is not often provided a definitive assessment due to the expert's clinical experience and judgment during review. Although there are considerable studies about the segmentation of blood smear images in the literature, there is no method to segment all blood cells. In this study, a new segmentation algorithm is proposed, which automatically extracts leukocyte, erythrocyte and thrombocyte cells from peripheral blood smear images. Purpose of this study here is to make highly accurate and complete blood count. The algorithm treats each image as a universal set and represents each object in the image as a subset as a result of the applied operations. In the developed method, leukocytes and thrombocytes achieve better success than other studies. However, it has been observed that the average success rate of stacked erythrocytes decreases. Statistical tests of the developed method were performed using 200 blood smear images in experimental studies. According to the obtained results, it is seen that high accuracy (leukocyte 99.86%, thrombocyte 98.4%, erythrocyte 93.4%) and precision (leukocyte 94.77%, thrombocyte 90.14%, erythrocyte 95.88%) were achieved in all three blood cells.

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“…Maitra [5] proposed an approach to automatic segmentation and counting of red blood cells in microscopic blood cell images using Hough Transform. Madhloom [6] new method that integrates color features with the morphological reconstruction to localize and isolate lymphoblast cells from a microscope image that contains many cells. Djawad [7] conducted an experiment to analyze the effect methanol extract of clove leaf on the profile of superoxide dismutase (SOD) in the rabbits liver under hypercholesterolemic condition.…”

Section: Introductionmentioning

confidence: 99%

Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell Nucleus based on Morphological Image

Supriyanti¹,

Chrisanty²,

Ramadhani³

et al. 2018

IJECE

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Hematology tests are examinations that aim to know the state of blood and its components, one of which is leukocytes. Hematologic examinations such as the number and morphology of blood generally still done manually, especially by a specialist pathologist. Despite the fact that today there is equipment that can identify morphological automatically, but for developing countries like Indonesia, it can only be done in the capital city. Low accuracy due to the differences identified either by doctors or laboratory staff, makes a great reason to use computer assistance, especially with the rapid technological developments at this time. In this paper, we will emphasize our experiment to screen leucocyte cell nucleus by identifying the contours of the cell nucleus, diameter, circumference and area of these cells based on digital image processing techniques, especially using the morphological image. The results obtained are promising for further development in the development of computer-aided diagnosis for identification of leukocytes based on a simple and inexpensive equipment.

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

Cited by 56 publications

References 10 publications

Peripheral blood smear image analysis: A comprehensive review

Peripheral blood smear image analysis: A comprehensive review

A new approach for fully automated segmentation of peripheral blood smears

Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell Nucleus based on Morphological Image

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