Classification of three types of red blood cells in peripheral blood smear based on morphology

Soltanzadeh, Ramin; Rabbani, Hossein

doi:10.1109/icosp.2010.5655754

Cited by 26 publications

(16 citation statements)

References 5 publications

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“…al [8] used the histogram thresholding to distinguish the nucleus of the leukocyte or white blood cells from the rest of the cells in the image. Ramin Soltanzadeh [9] has purposed feature extraction technique based on morphology in his three blood cell's experiments. 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.…”

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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“…These artifacts and other the cell tissues merge with background part of the image due to low intensity shown in figure 6. We use K-means algorithm to cluster the object feature F defined in Equation (2). The purpose of K-means clustering is that the clusters of similar items are grouped.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Thus requiring unsupervised methods that able to perform automatic cell segmentation in an objective and efficient way is necessary. Many automatic segmentation methods have been proposed, Segmentation of white blood cells (WBC) into nucleus [1], red blood cell classification [2,9,17] extraction and segmentation of sputum cells for lung cancer [3,4,5] however the degree of supervision is involved either in selected restricted region [6] or they used predefined size [17] of the cell tissues. We proposed a supervised approach to identify the malaria parasite from light microscopy images.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Unsupervised identification of malaria parasites using computer vision

Khan

Pervaz

Latif

et al. 2014

2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE)

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Malaria in human is a serious and fatal tropical disease. This disease results from Anopheles mosquitoes that are infected by Plasmodium species. The clinical diagnosis of malaria based on the history, symptoms and clinical findings must always be confirmed by laboratory diagnosis. Laboratory diagnosis of malaria involves identification of malaria parasite or its antigen / products in the blood of the patient. Manual diagnosis of malaria parasite by the pathologists has proven to become cumbersome. Therefore, there is a need of automatic, efficient and accurate identification of malaria parasite.In this paper, we proposed a computer vision based approach to identify the malaria parasite from light microscopy images. This research deals with the challenges involved in the automatic detection of malaria parasite tissues. Our proposed method is based on the pixel based approach. We used K-means clustering (unsupervised approach) for the segmentation to identify malaria parasite tissues.

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“…The algorithm presented in the present study has the objective of reaching all these points, solving them in less processing time and computational cost associated with high accuracy in the detection of erythrocytes and leucocytes, as well as a better execution performance in comparison to other works as (ARIVU; SATHIYA, 2012; GUITAO et al, 2009;HEIDI et al, 2011;MANSOR, 2012;MAZALAN;RAZAK, 2013;MOGRA;SRIVASTAVA, 2014;SAHASTRABUDDHE;AJIJ, 2016;SOLTANZADE et al, 2010).…”

Section: Reinaldo Padilha Françamentioning

confidence: 94%