Determination and Classification of Blood Types using Image Processing Techniques

Ravindran, G.; Tienaah, Titus; Pravin, M. C.; Pandiyan, P.

doi:10.5120/ijca2017912592

Cited by 21 publications

(10 citation statements)

References 4 publications

(2 reference statements)

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“…The following techniques for image processing: preprocessing, Grayscale image, image segmentation, feature extraction, and classification have applied to detect blood groups for more than one person at a single stretch in a short time by Banu et al [32]. Image processing techniques like thresholding, morphological operations, HSL Luminance plane, and quantification have used for blood group identification by Ravindran et al [33].…”

Section: Related Workmentioning

confidence: 99%

Blood Group Classification System Based on Image Processing Techniques

Shaban¹,

Elsheweikh²

2022

Intelligent Automation &Amp; Soft Computing

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The present paper proposes a novel system that automatically classifies the eight different blood groups according to the ABO and Rh blood group systems. The proposed system is developed by applying MATLAB's image processing techniques on the blood sample images. These images are acquired from the laboratory using the slide test. It utilizes a mean filter for removing noise from blood sample images. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancing the image characteristics analysis. The proposed system also utilizes the automated threshold strategy (Otsu's approach) for obtaining the blood samples binary images. Since, adding the three antigens (A, B, D) to the blood samples works on clumping the samples or not, and this, in turn, works on the variation number of objects and holes in the different blood groups' samples. Therefore, the "Bwboundaries" Matlab function is applied for the first time in the proposed system to count the number of objects and holes for blood group classification. The database used in the proposed system consists of 600 images collected from different laboratories. The suggested system has many advantages such as simplicity, speeding up processing time, reducing human interference and errors, decreasing the risk of transfusion reactions, and high accuracy, even in remote places. As well, it can be used by a lab technician or any user who doesn't have any previous knowledge of the blood group detection technique. In addition to using this system to assist in several applications such as premarital tests, blood transfusion as well as paternity tests.

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Section: Related Workmentioning

confidence: 99%

Blood Group Classification System Based on Image Processing Techniques

Shaban¹,

Elsheweikh²

2022

Intelligent Automation &Amp; Soft Computing

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“…The extraction of minutiae turns out to be even more testing on account of the commotion present and lack of difference in the picture. Ravindran et al (2017) proposed work taken blood sample images and pre-processed it by using various techniques such as color plane extraction, color to gray image conversion. These blood image pre-processing terms can dramatically increase the uniformity of a visual investigation of collected samples.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It was Henry Fauld in 1880 who first experimentally proposed the singularity and uniqueness of fingerprint. Herschel (Ravindran et al 2017 ) added to the establishment of current fingerprinting identification. In the nineteenth century Sir Francis Galton (McBean et al 2014 ) directed broad investigations and ordered the sorts of fingerprints relying on essential example as loops, whorls and arches.…”

Section: Introductionmentioning

confidence: 99%

An association between fingerprint patterns with blood group and lifestyle based diseases: a review

Patil

Ingle

2020

Artif Intell Rev

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In the current era of the digital world, the hash of any digital means considered as a footprint or fingerprint of any digital term but from the ancient era, human fingerprint considered as the most trustworthy criteria for identification and it also cannot be changed with time even up to the death of an individual. In the court of law, fingerprint-proof is undeniably the most dependable and acceptable evidence to date. Fingerprint designs are exclusive in each human and the chance of two individuals having identical fingerprints is an exceptional case about one in sixty-four thousand million also the fingerprint minutiae patterns of the undistinguishable twins are different, and the ridge pattern of each fingertip remain unchanged from birth to till death. Fingerprints can be divided into basic four categories i.e. Loop, whorl, arch, and composites, nevertheless, there are more than 100 interleaved ridge and valleys physiognomies, called Galton's details, in a single rolled fingerprint. Due to the immense potential of fingerprints as an effective method of identification, the present research paper tries to investigate the problem of blood group identification and analysis of diseases those arises with aging like hypertension, type 2-diabetes and arthritis from a fingerprint by analyzing their patterns correlation with blood group and age of an individual. The work has been driven by studies of anthropometry, biometric trademark, and pattern recognition proposing that it is possible to predict blood group using fingerprint map reading. Dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. Apart from its use in predicting the diagnosis of disease; dermatoglyphics is also used in forensic medicine in individual identification, physical anthropology, human genetics and medicine. However, the Machine and Deep Learning techniques, if used for fingerprint minutiae patterns to be trained by Neural Network for blood group prediction and classification of common clinical diseases arises with aging based on lifestyle would be an unusual research work.

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“…It is insufficient to perform the most basic and necessary medical procedure in a completely analogous environment. There are also several techniques, such as microplate testing and gel centrifugation [5,7]. These procedures are expensive and need to be performed by people with strong skills and some special equipment.…”

Section: Introductionmentioning

confidence: 99%

Dijital Görüntüler Kullanılarak Kan Grubunun Görüntü İşleme Tabanlı Tespiti

Atici

Koçer

Kader³

2020

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Kan gruplandırılması, temel tıbbi prosedürlerin çoğu için ilk ve en önemli gerekliliktir. Kan grubunu tespit etmede kullanılan geleneksel teknikte uzman, kan örneğine anti-serum madde karıştırarak kandaki renk ve biçim değişikliğini izler ve kan grubu ile Rh faktörünü belirler. Bu işlem uzman tarafından gün boyunca yapılmaktadır ve yorgunluk, gözden kaçırma gibi sebeplerden dolayı hataya açıktır. Bu çalışmada, görüntü işleme teknikleri kullanılarak kan grubunun otomatik olarak tespit edilmesi amaçlanmıştır. Bunun için gerçek hasta kanlarının dijital görüntüleri üzerinde bir dizi görüntü işleme tekniği uygulanmıştır. Alınan kan görüntüleri üç bölüme ayrıldıktan sonra öncelikle RGB-Gri seviye-Siyah/Beyaz dönüşümü yapılmıştır. Ardından morfolojik işlemler uygulanarak kan bölgesi segmente edilmiş ve kenar sınır çizgileri işaretlenmiştir. Son aşamada ise beyaz piksel yoğunluğu, bölge içerisindeki nesne sayısı ve kenar piksel sayısı tespit edilerek kan grubu ve Rh faktörü belirlenmiştir.

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Determination and Classification of Blood Types using Image Processing Techniques

Cited by 21 publications

References 4 publications

Blood Group Classification System Based on Image Processing Techniques

Blood Group Classification System Based on Image Processing Techniques

An association between fingerprint patterns with blood group and lifestyle based diseases: a review

Dijital Görüntüler Kullanılarak Kan Grubunun Görüntü İşleme Tabanlı Tespiti

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