A counting method for complex overlapping erythrocytes-based microscopic imaging

Abstract

Red blood cell (RBC) counting is a standard medical test that can help diagnose various conditions and diseases. Manual counting of blood cells is highly tedious and time consuming. However, new methods for counting blood cells are customary employing both electronic and computer-assisted techniques. Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image. In this research work, an approach for erythrocytes counting is proposed. We employed a classi¯cation before counting and a new segmentation idea was implemented on the complex overlapping clusters in a microscopic smear image. Experimental results show that the proposed method is of higher counting accuracy and it performs much better than most counting algorithms existed in the situation of three or more RBCs overlapping complexly into a group. The average total erythrocytes counting accuracy of the proposed method reaches 92.9%.

Automated marker identification using the Radon transform for watershed segmentation

“…Using a non-optimised computer programme code on a Dual Core Intel Pentium E5300 central processing unit at 2.6 GHz with 2 GB random access memory, and 64 bits Windows 7 Home Premium operating system, the average running time of our method was 20.29 s/image. This is less than a half of the excellent time taken with Wei et al [28] using a comparable processor, which took 49.86 s/image to identify the markers of individual cells using the watershed transform and K-means for detecting and splitting clustered cells.…”

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

“…Blood cells were easily identified by this method using a unique color of every component. Wei et al [ 157 ] proposed a method to detect and count overlapped RBCs in microscopic blood smear images. The H and S components were used to differentiate between WBCs and segmented RBCs.…”

“…of images(Stain) Accuracy(%) Remarks Ref. K-means clustering, WT 60 (Giemsa )100 (Wright–Giemsa) 93–98.9 Robustness is not explained [ 11 , 140 , 157 ] Iterative structured circle detection, circlet transform 100 95.3 Incorrect hole filling leads to errors To improve initial RBCs mask for accurate segmentation [ 28 , 143 ] Graph algorithm 98 99 Considered only non-overlapped cells [ 133 ] Parametric template matching, PCNN 900 cells 90–95.7 Require prior knowledge about the appearance of the cell [ 16 , 46 , 98 ] YOLO algorithm 364 96.1 Satisfactory performance [ 20 ] HSV conversion, morphological operations 200 (Giemsa) 96 Used uniform staining and illumination [ 125 ] Pixel relationship 10 (MGG) 83 Occluded objects are rejected before the later stages [ 80 ] Canny, LOG, Sobel 20–30 85–93 Normal RBCs Less samples [ …”

Analysis of Vision-based Abnormal Red Blood Cell Classification

“…Therefore overlapping cells were separated out from the database. In the literature various measures have been used to identify single and overlapping cells (area / convexity / eccentricity (Wei et al, 2015;Abu-Qasmieh, 2018)) but as there were more class types and very different cell shapes in this dataset, this was not sufficient for adequate identification.…”