Red Blood Cells Estimation Using Hough Transform Technique

Mahmood, Nasrul Humaimi; Mansor, Muhammad Asraf

doi:10.5121/sipij.2012.3204

Cited by 49 publications

(37 citation statements)

References 9 publications

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“…19 The magnitude of tissue trauma can be evaluated based on manual stereology of histological slides, through semiquantitative methods, or by automatic analysis of digital images [20][21][22] where stains are used to detect specific cells of interest. For example, analyzing the presence of white blood cells (WBCs) in the tissue is one commonly used inflammation marker.…”

mentioning

confidence: 99%

Skin Blood Perfusion and Cellular Response to Insertion of Insulin Pen Needles With Different Diameters

Præstmark

Jensen

Stallknecht

et al. 2014

J Diabetes Sci Technol

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Approximately 382 million people worldwide have diabetes, 1 half of the prevalent cases are not known or diagnosed, half of those diagnosed are not treated, and half of those treated are not controlled. 2,3 One of the reasons for poor treatment compliance is injection anxiety causing 20% of insulin users to sometimes skip their injections, and 10% to restrict their number of injections. 4 As many as 94% of insulin users exhibit symptoms of anxiety, distress, or phobia around blood and injury from injections, 5 22% of insulin users have to mentally prepare themselves for injections, 6 and 33% of insulin users dread their injections. 7 This underlines why it is of high importance to develop needle designs that cause as little fear, injury, and pain as possible. Pain perception is one of the preferred methods to evaluate new needle design. 8-18 Studies have shown how needle diameter correlates with both the magnitude of the perceived pain, typically measured on visual analog scales (VASs), and with pain occurrence, that is, how often the needle causes pain sensation. 11-13 However, pain is a subjective measure with a large number of biasing variables causing data with high variance. Therefore, a high sample size is needed to detect differences in pain, which makes it both costly and time-consuming to carry out the clinical trials. One alternative way to obtain information about the needle impact on tissue is to use animal models, where histology can be used to assess tissue trauma from, for example, a needle insertion. The needle insertion can cause tissue bleeding and initiate inflammation in the tissue. Pig models are especially useful when examining skin disease and wound 531099D STXXX10.

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mentioning

confidence: 99%

Skin Blood Perfusion and Cellular Response to Insertion of Insulin Pen Needles With Different Diameters

Præstmark

Jensen

Stallknecht

et al. 2014

J Diabetes Sci Technol

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“…To identify elliptical shaped cells, Hough Circle Transformation [13] was used together with the SAF factor as given in equation (1).…”

Section: A Elliptocytes Identification and Classificationmentioning

confidence: 99%

“…Red Blood Cells with small pale centers [13] are determined using the CP and AP factors as referenced in Fig. 1.…”

Section: Hypochromic Microcytes Identification and Classificationmentioning

confidence: 99%

“…The binary image and the interior gap filled image is used to get the absolute difference and center pallor was extracted using a morphology operations erosion and dilation with much smaller structuring element [15]. Finally Hough circle transformation [13] was applied to isolate the sphere shaped cells. …”

Section: Hereditary Spherocytes Identification and Classificationmentioning

confidence: 99%

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Automatic anemia identification through morphological image processing

Chandrasiri

Samarasinghe

2014

7th International Conference on Information and Automation for Sustainability

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Though blood cell manipulation has been an interesting research area for many years, most of the techniques presented in literature produce poor segmentation results for images with high overlapped blood cells. In this paper, we introduce a fully automatic low cost and accurate system to identify four common types of anemia and report on blood cell count. The results of our system indicate a good impact with the manually processed results of 99.678% accuracy of Red Blood Cell count. The diagnosis of Elliptocytes, Microcytes, Macrocyte and Spherocytes anemia result in the range of 91%-97% accuracy.

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“…tear drop, holly jolly, moon like RBCs, etc. (Mahmood et al, 2013;grietinfo.in, 2013;Mahmood & Mansor, 2012;Ramesh et al, 2012) Some studies achieved a high degree of accuracy, but involuted human intervention means semi-automatic behavior (Abbas et al, 2014;Abbas et al, 2015).…”

Section: Clustered Red Blood Cells Splitting Via Boundary Analysis Inmentioning

confidence: 99%

Clustered Red Blood Cells Splitting via Boundary Analysis in Microscopic Thin Blood Smear Digital Images

Abbas

Abdullah²,

Mohamad

et al. 2015

IJTech

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Clustered Red Blood Cells are observed very frequently in the thin blood smear digital images. Separating clustered Red Blood Cells from the single Red Blood Cells and splitting of clustered Red Blood Cells into single Red Blood Cells is a challenging job in the computer-assisted diagnosis of blood for any disorder in many diseases like Complete Blood Count Test, Anemia, Leukemia and Malaria etc. The mentioned problems are highly laborious in manual microscopy for the hematologists. Many techniques currently existing for the solution suffer from both under-and over-splitting problems when highly complex clusters of Red Blood Cells occur. In addition, the existing techniques are not computationally efficient. In this paper, we address the aforementioned problems, firstly by considering the boundaries of the convex hulls of clustered Red Blood Cells and secondly, by splitting the boundaries according to the number of Red Blood Cells in relation to distance measures. Furthermore, we draw circles using a mid-point circle algorithm at each boundary cleavage to give an illusion of the Red Blood Cells. The test results of the proposed technique on a standard online dataset are presented in two ways. Statistically first of all by achieving an average recall of 0.964 and precision of 0.970 while their F-measure achieved is 0.962 as well as secondly through ground truth data with visual inspections.

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Red Blood Cells Estimation Using Hough Transform Technique

Cited by 49 publications

References 9 publications

Skin Blood Perfusion and Cellular Response to Insertion of Insulin Pen Needles With Different Diameters

Skin Blood Perfusion and Cellular Response to Insertion of Insulin Pen Needles With Different Diameters

Automatic anemia identification through morphological image processing

Clustered Red Blood Cells Splitting via Boundary Analysis in Microscopic Thin Blood Smear Digital Images

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