Leukocyte segmentation and classification in blood-smear images

Ramoser, Herbert; Laurain, Vincent; Bischof, Horst; Ecker, Rupert

doi:10.1109/iembs.2005.1617200

Cited by 61 publications

(46 citation statements)

References 8 publications

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“…To achieve suffi− ciently precise description of cells' behaviour, as the first step of analysis, the image segmentation should be per− formed with relevant precision adjusted to the aim of inves− tigation [18]. Well known and simple segmentation me− thods [19][20][21][22][23], such as various types of thresholdings, gra− dient and edge examination and clustering are effective for ideal images. An ideal image means in this case an image which is acquired by having the possibility of adjusting parameters of acquisition.…”

Section: Subject Of Papermentioning

confidence: 99%

Multistage morphological segmentation of bright-field and fluorescent microscopy images

Korzyńska

Iwanowski

2012

Opto-Electronics Review

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This paper describes the multistage morphological segmentation method (MSMA) for microscopic cell images. The proposed method enables us to study the cell behaviour by using a sequence of two types of microscopic images: bright field images and/or fluorescent images. The proposed method is based on two types of information: the cell texture coming from the bright field images and intensity of light emission, done by fluorescent markers. The method is dedicated to the image sequences segmentation and it is based on mathematical morphology methods supported by other image processing techniques. The method allows for detecting cells in image independently from a degree of their flattening and from presenting structures which produce the texture. It makes use of some synergic information from the fluorescent light emission image as the support information. The MSMA method has been applied to images acquired during the experiments on neural stem cells as well as to artificial images. In order to validate the method, two types of errors have been considered: the error of cell area detection and the error of cell position using artificial images as the “gold standard”.

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Section: Subject Of Papermentioning

confidence: 99%

Multistage morphological segmentation of bright-field and fluorescent microscopy images

Korzyńska

Iwanowski

2012

Opto-Electronics Review

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“…It is difficult to compare these results with other studies because the images databases are not the same. In any case, the segmentation results that are announced in [8] have a 95% success rate. …”

Section: Segmentationmentioning

confidence: 99%

“…Trying to put this ability into a machine has been very hard. Some researches [8] have shown very good results using an unsupervised methods in the segmentation process. They assume some color saturation relations with the leukocytes and the rest of the elements.…”

Section: Current Techniquesmentioning

confidence: 99%

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Segmentation and Classification of Leukocytes Using Neural Networks: A Generalization Direction

Rodrigues

Ferreira

Monteiro

2008

Studies in Computational Intelligence

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Summary. 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.

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“…As the research further develops, color image segmentation attracts more and more attention. Some color and mathematical morphology methods are applied in segmentation of cytological images [10,11]. Some algorithms are based on the saturation and green components' distribution [12].…”

Section: Introductionmentioning

confidence: 99%

Preprocessing of Separating Leukocytes Based on Setting Parameters of Lightness Transformation

Cai¹,

Luo²,

Cai³

et al. 2013

JSIP

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This paper proposed a new algorithm to separate leukocytes from cytological image by setting parameters of lightness transformation based on the RGB color space, which can make the targets' color in different areas. In our procedure, an operator is employed in using color features. According to their histogram distribution of hue component in HSL color space after enhancing the contrast of image in RGB color space, the threshold of segmentation between leukocyte and erythrocyte could be achieved well. Especially, this algorithm is more efficient than monochrome for leukocyte segmentation, and the results of experiments show that it provides a good tool for cytological image, which can increase accuracy of segmentation of leukocyte.

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Leukocyte segmentation and classification in blood-smear images

Cited by 61 publications

References 8 publications

Multistage morphological segmentation of bright-field and fluorescent microscopy images

Multistage morphological segmentation of bright-field and fluorescent microscopy images

Segmentation and Classification of Leukocytes Using Neural Networks: A Generalization Direction

Preprocessing of Separating Leukocytes Based on Setting Parameters of Lightness Transformation

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