Improved red blood cell counting in thin blood smears

Berge, Heidi; Taylor, Dale L.; Krishnan, Sriram; Douglas, Tania S.

doi:10.1109/isbi.2011.5872388

Cited by 31 publications

(28 citation statements)

References 9 publications

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“…Based on morphology of the cells, the mass center of each cell in the images and then find the distance of each pixel on an edge from the center. Heidi Berge [10] has purposed the segmentation red blood cells in a thin blood smear image which is based on the Zack's Method [11]. This method is one of the approached to determine the red blood cells tresholding where a line is drawn between the two peaks and between these two peaks, they used the point which is furthest from the drawn line as a threshold for red blood cell.…”

Section: Related Workmentioning

confidence: 99%

Red Blood Cells Estimation Using Hough Transform Technique

Mahmood

Mansor²

2012

SIPIJ

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The number of red blood cells contributes more to clinical diagnosis with respect to blood diseases. The aim of this research is to produce a computer vision system that can detect and estimate the number of red blood cells in the blood sample image. Morphological is a very powerful tool in image processing, and it is been used to segment and extract the red blood cells from the background and other cells. The algorithm used features such as shape of red blood cells for counting process, and Hough transform is introduced in this process. The result presented here is based on images with normal blood cells. The tested data consistsof 10 samples and produced the accurate estimation rate closest to 96% from manual counting.

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Section: Related Workmentioning

confidence: 99%

Red Blood Cells Estimation Using Hough Transform Technique

Mahmood

Mansor²

2012

SIPIJ

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“…Grey scale granulometry is a method that estimates the size of objects in an image using a series of morphological openings with a structuring element of a fixed shape (in the case of RBCs, this shape was circular) that has been used in studies on RBC size estimation [4,5]. The resultant pattern spectrum shows the distribution of object sizes within the image.…”

Section: Rbc Size Estimationmentioning

confidence: 99%

“…Berge et al [4], extending the work of Le et al [8] suggested iteratively using Zack's thresholding algorithm to determine the threshold between the two peaks and extract the foreground.…”

Section: Foreground Extraction 1) Determining the Thresholdmentioning

confidence: 99%

“…Berge et al [4] suggested a second, more accurate threshold, found by using the line connecting the first threshold (T1 in Figure 2) to the line connecting the peaks and finding the point furthest from this second line (see L2 in Figure 2) resulting in the final threshold T2 in Figure 2.…”

Section: Foreground Extraction 1) Determining the Thresholdmentioning

confidence: 99%

“…Current research involves a pre-processing stage that optimises the input image for automation, the extraction and segmentation of red blood cells and the detection of stained pixels. These operations can be performed by morphological operations, thresholding and statistical classification using morphology, texture and colour [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated detection of malaria in Giemsa-stained thin blood smears

Mushabe

Dendere

Douglas

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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The current gold standard of malaria diagnosis is the manual, microscopy-based analysis of Giemsa-stained blood smears, which is a time-consuming process requiring skilled technicians. This paper presents an algorithm that identifies and counts red blood cells (RBCs) as well as stained parasites in order to perform a parasitaemia calculation. Morphological operations and histogram-based thresholding are used to extract the red blood cells. Boundary curvature calculations and Delaunay triangulation are used to split clumped red blood cells. The stained parasites are classified using a Bayesian classifier with their RGB pixel values as features. The results show 98.5% sensitivity and 97.2% specificity for detecting infected red blood cells.

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Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

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Splitting the rouleaux RBCs from single RBCs and its further subdivision is a challenging area in computer-assisted diagnosis of blood. This phenomenon is applied in complete blood count, anemia, leukemia, and malaria tests. Several automated techniques are reported in the state of art for this task but face either under or over splitting problems. The current research presents a novel approach to split Rouleaux red blood cells (chains of RBCs) precisely, which are frequently observed in the thin blood smear images. Accordingly, this research address the rouleaux splitting problem in a realistic, efficient and automated way by considering the distance transform and local maxima of the rouleaux RBCs. Rouleaux RBCs are splitted by taking their local maxima as the centres to draw circles by mid-point circle algorithm. The resulting circles are further mapped with single RBC in Rouleaux to preserve its original shape. The results of the proposed approach on standard data set are presented and analyzed statistically by achieving an average recall of 0.059, an average precision of 0.067 and F-measure 0.063 are achieved through ground truth with visual inspection.

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Improved red blood cell counting in thin blood smears

Cited by 31 publications

References 9 publications

Red Blood Cells Estimation Using Hough Transform Technique

Red Blood Cells Estimation Using Hough Transform Technique

Automated detection of malaria in Giemsa-stained thin blood smears

Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs

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