An improved methodology for blood cell counting

Tulsani, Hemant; Gupta, Rashmi; Kapoor, Rajiv

doi:10.1109/mspct.2013.6782094

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…In this connection, various color space conversion is used for segmenting white, red blood cells and the platelets. [7] [8]. Traditionally there are several segmentation methods like Adaptive thresholding, Otsu segmentation, K means Clustering etc… From the literature review it is observed that the segmentation accuracy of the traditional methods is less.…”

Section: Introductionmentioning

confidence: 99%

Leukaemia Detection in Microscopic Imagery using Optimization Algorithm

Dasu¹,

Subbaiah²

2018

IJET

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In this paper, automated approach of blood cancer detection is proposed. Usually microscopic images examined by experts manually are time consuming and less accuracy. The automated blood cancer detection system analyses the microscopic image and overcomes these drawbacks. The proposed system extracts the features of the image and applies filtering techniques. In this paper proposed method is cuckoo search optimization algorithm which is used in line with segmentation. The features of segmented image can be obtained from Scale invariant feature transform. Some of the features like PSNR, sensitivity, accuracy, etc. are calculated for leukemia detection. The performance is compared with the existing method ACO. The proposed system is tested on image dataset and 94.24% accuracy is achieved. The proposed system is successfully implemented in MATLAB.

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Section: Introductionmentioning

confidence: 99%

Leukaemia Detection in Microscopic Imagery using Optimization Algorithm

Dasu¹,

Subbaiah²

2018

IJET

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“…Complete blood count (CBC), also known as blood counting [1], is a basic screening test which is performed frequently in laboratories [2]. It counts the quantity of different types of major cells in the blood sample, which are red blood cells (RBC), white blood cells (WBC) and platelets [3].…”

Section: Introductionmentioning

confidence: 99%

Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from Blood Smeared Image

Kit

Tomari

Zakaria

et al. 2017

IJECE

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<span>Blood cells diagnosis is becoming essential to ensure a proper treatment can be proposed to a blood related disease patient. In current research trending, automated complete blood count analysis system is required for pathologists or researchers to count the blood cells from the blood smeared images. Hence, a portable mobile-based complete blood count (CBC) analysis framework with the aid of microscope is proposed, and the smartphone camera is mounted to the viewing port of the light microscope by adding a smartphone support. Initially, the blood smeared image is acquired from a light microscope with objective zoom of 100X magnifications view the eyepiece zoom of 10X magnification, then captured by the smartphone camera. Next, the areas constitute to the WBC and RBC are extracted using combination of color space analysis, threshold and Otsu procedure. Then, the number of corresponding cells are counted using topological structural analysis, and the cells in clumped region is estimated using Hough Circle Transform (HCT) procedure. After that, the analysis results are saved in the database, and shown in the user interface of the smartphone application. Experimental results show the developed system can gain 92.93% accuracy for counting the RBC whereas 100% for counting the WBC.</span>

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“…Results are compared with Otsu's and Zack thresholding approach which shows that IFS gives good result than other methods but the main disadvantage of this method is complex computation which is timeconsuming. H.Tulsani et al [7] have presented an improved method for blood cell counting. They have used YCbCr color space model which overcomes issues of illumination.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images

Bhavnani¹,

Jaliya²

2016

IJIGSP

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In the biomedicine field, blood cell analysis is the first step for diagnosis of many of the disease. The first test that is requested by a doctor is the CBC (Complete Blood cell Count). Microscopic image of blood stream contains three types of blood cells: Red Blood Cells (RBCs), White Blood Cells (WBCs) and platelets. Earlier counting of blood cell was done manually which was inaccurate and depends on operator's skill. Counting of blood cells using image processing provides cost effective and accurate result than manual counting. During the counting process, the splitting of clumped cell is the most challenging issue. This paper represents segmentation and counting of RBCs and WBCs from microscopic blood sample images. Segmentation is done using Otsu's thresholding and morphological operations. Counting of cells is done using geometric features of cells. RBCs contain clumped cells which make the task of counting of cells accurately very challenging. For counting of RBCs, two different methods are used: 1) Watershed segmentation 2) Circular Hough Transform. Comparison of both this method is shown for randomly selected images. The performance of counting methods is also analyzed by comparing it with results obtained by manual counts.

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An improved methodology for blood cell counting

Cited by 10 publications

References 14 publications

Leukaemia Detection in Microscopic Imagery using Optimization Algorithm

Leukaemia Detection in Microscopic Imagery using Optimization Algorithm

Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from Blood Smeared Image

Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images

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