CBC and DBC Counter Using Image Processing and Analysis

Cruz, Jennifer C. Dela; Jennifer, C.; Fausto,; Janette, C.; Agatep,; Errol, L.; Manlangit,; John, Younghun; Valenzuela,; Renielle, F. John

doi:10.1109/icspcc50002.2020.9259536

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The minimum and maximum values were automatically set to permit certain sizes of blobs, while the others were removed. Next, the binary image g(x, y) could be interpreted as (5), where T = threshold value.…”

Section: Area Thresholdingmentioning

confidence: 99%

“…The manual counting process could also be time-consuming, burdensome, and exhausting for haematologists [4], particularly when there are numerous patients on one occasion. Additionally, the manual platelet count may lead to miscounting the number of platelets due to human error [5]. The platelet may have overlapping blood cells that make counting with the naked eye difficult.…”

Section: Introductionmentioning

confidence: 99%

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Automated Platelet Counter with Detection Using K-Means Clustering

Ibrahim,

Fauzi,

Mangshor

et al. 2023

AETiC

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Platelet is a blood cell type that is stored and circulated in the human body. It acts as a blood thickening agent and prevents blood from overflowing whenever bleeding occurs. An excessive or inadequate number of platelets could lead to platelet-related diseases. The current practice of platelet counting involves the manual counting process using a haemocytometer, Wright’s Stain which uses the dyes to facilitate the differentiation of blood cell types, and a tally counter. Yet, this process can be time-consuming, demanding, and exhausting for haematologists, and likely to be prone to errors. Thus, this paper presents a study on automated platelet counter and detection using image processing techniques. The K-Means Clustering was employed to count and detect the presence of platelets in microscopic blood smear images. Several processes were performed prior to the K-means clustering, including image enhancement and YCbCr image formatting. Subsequently, image masking, as well as area thresholding were applied to eliminate every unwanted entity and highlight the visibility of the platelets before the number of platelets could be detected and counted. A comparative experiment was designed in which the K-Means Clustering platelet count and detection were compared with the actual number of platelets reported by haematologists. The platelet counts and detection were categorized into three detection categories which are Less Detection (LD), Accurate Detection (AD), and Over Detection (OD). The proposed study was evaluated to 90 testing platelet images. Out of the 90 testing images, 75 platelet images were perfectly counted and detected which returned 91.67% of accuracy. This signifies that the K-Means Clustering algorithm was discovered to be efficient and dependable for automated platelet counter and detection

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Section: Area Thresholdingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%