Automatic segmentation, counting, size determination and classification of white blood cells

Nazlibilek, Sedat; Karacor, Deniz; Ercan, Tuncay; Sazlı, Murat H.; Kalender, Osman; Ege, Yavuz

doi:10.1016/j.measurement.2014.04.008

Cited by 141 publications

(72 citation statements)

References 17 publications

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“…Different from 2-D optical blood images, hyperspectral blood images have three dimensions which contain both spatial and spectral information [5,27]. The hyperspectral imaging system outputs 60 single bands for one scene and the data was stored in band sequential (BSQ) format, ranging from 550 nm to 1000 nm.…”

Section: Hyperspectral Imagementioning

confidence: 99%

“…Then, based on the size distribution of the blood cells, the cytoplasm is segmented using basic operations such as thresholding and morphological opening. S. Nazlibilek et al converted an RGB blood smear image into a grayscale image and used Otsu's method to convert the grayscale image to a binary image [5]. The individual images were applied to a neural network-based classifier to classify the cells into the five types.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A spectral and morphologic method for white blood cell classification

Wang

Zhou

et al. 2016

Optics & Laser Technology

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Section: Hyperspectral Imagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A spectral and morphologic method for white blood cell classification

Wang

Zhou

et al. 2016

Optics & Laser Technology

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“…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%

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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“…Radial-based cell formation algorithm [14] detects overlapping RBCs and cervical cells but it has not yet been tested on overlapping WBCs in blood smear images. Automatic counting and classification of WBCs based upon morphological features in [15] can not detect irregular and overlapped WBCs accurately. In [16], edge detection and watershed segmentation (WS) have been utilized to separate overlapping WBCs but mostly the separation of overlapping WBCs suffers from over-segmentation even after the application of post-processing steps.…”

Section: Introductionmentioning

confidence: 99%