A method based on multispectral imaging technique for White Blood Cell segmentation

Guo, Ningning; Zeng, Libo; Wu, Qin

doi:10.1016/j.compbiomed.2005.10.003

Cited by 73 publications

(27 citation statements)

References 4 publications

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“…The accuracy of the leucocyte segmentation directly affects feature extraction and classification. The difficulties of leucocyte segmentation are due to the following reasons (Guo et al 2006): the collected cell image often contains the leucocytes, the erythrocytes, the platelets and the various types of grunge; the image is often contaminated by the staining and the illumination inconsistencies; in particular, some of the cells overlap with each other, and there is often not a clear boundary between the nucleus and the cytoplasm. Some segmentation methods for blood cell images were proposed, most of which are region based or edge based.…”

Section: Introductionmentioning

confidence: 99%

“…The feature space clustering techniques can avoid the variety and complexity in an image space, but they require a number of clusters to be determined in advance. Guo et al (2006) proposed a leucocyte segmentation method by means of the multispectral imaging technique; however, this method is very time consuming and the accuracy depends on the resolution of wavelength. Moreover, the technique needs an expensive specific multispectral imaging microscope apparatus.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of overlapping leucocyte images with phase detection and spiral interpolation

Cui

2012

Computer Methods in Biomechanics and Biomedical Engineering

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Leucocyte segmentation is one of the most crucial functionalities for an automatic leucocyte recognition system. In this paper, an algorithm is proposed to segment the leucocytes from the overlapping cell images. It consists of two main steps. The first step involves generation of a combined image based on the saturation and green channels (CIBSGC) by means of the different distribution characteristics of the leucocyte nucleus. A weight coefficient is used to adjust the CIBSGC for extracting the nucleus and estimating the location of the leucocyte. Second, a method of phase detection and spiral interpolation identifies the overlapping regions of cells and determines the leucocyte edge curve. The performance is evaluated by three parameters: sensitivity, positive predictive value and pixel number error. Experimental results validate that the proposed algorithm can successfully segment the overlapping leucocyte with the satisfactory performance for two cell image datasets under different recording conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Segmentation of overlapping leucocyte images with phase detection and spiral interpolation

Cui

2012

Computer Methods in Biomechanics and Biomedical Engineering

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“…Common medical imaging techniques, such as, magnetic resonance imaging (MRI) [7], computerized tomography (CT) [8], ultrasound imaging [9], electron microscopic imaging [10], etc., are applied to assist doctors and pathologists for disease diagnosis. Pathologists use electron microscope to find protozoan parasites and diagnose the species of the protozoan parasites by their biomorphic, such as, nucleus numbers, cell tissue distribution and smoothness of cell membrane.…”

Section: Introductionmentioning

confidence: 99%

A protozoan parasite extraction scheme for digital microscopic images

Lai

Tseng

et al. 2010

Computerized Medical Imaging and Graphics

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“…[1] This has implications for both laboratory resources as well as diagnostic accuracy. As a result, many researchers have addressed problems related to microscopic image analysis of peripheral blood smears,[2345] and indeed developing image analysis systems for computer-assisted interpretations of peripheral blood smears is of a great importance. The goal of this paper is to provide a comprehensive review of the algorithms and methodologies used to analyze peripheral blood smear images.…”

Section: Introductionmentioning

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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A method based on multispectral imaging technique for White Blood Cell segmentation

Cited by 73 publications

References 4 publications

Segmentation of overlapping leucocyte images with phase detection and spiral interpolation

Segmentation of overlapping leucocyte images with phase detection and spiral interpolation

A protozoan parasite extraction scheme for digital microscopic images

Peripheral blood smear image analysis: A comprehensive review

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