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durable construction this philips led bulb is designed to give you maximum luminance with a minimum usage of energy.
متانة البنية صُمم مصباح ال اي دي من فيليبس ليمنحك أقصى قدر من النصوع مع الحد الأدنى من استخدام الطاقة،
contrast is the difference in luminance or color that makes an object (or its representation in an image or display) distinguishable.
التباين في البصريات (بالإنجليزية: contrast) هو الاختلاف في اللون أو الاختلاف في الظل الذي يجعل الأشياء واضحة في الصورة ويفرقها عن بعضها .
the jpeg and png image formats are capable of storing 16.7 million colors (equal to 256 luminance values per color channel).
الصور ذات تنسيق جي بي جي وبي إن جي قادرة على تخزين 16.7 مليون لون (ما يعادل 256 قيمة لكل قناة لون).
the actual vir signal contains three sections, the first having 70 percent luminance and the same chrominance as the color burst signal, and the other two having 50 percent and 7.5 percent luminance respectively.
إشارة vir الفعلي تحتوي على ثلاثة أقسام، الأول وجود 70 في المئة نصوع ونفس التلون كما إشارة اندفاع اللون، واثنين اخرين بعد 50 في المئة و 7.5 في المئة على التوالي نصوع.
the majority of automatic blood typing approaches include three main stages: preprocessing, feature extraction, and classification. in the preprocessing stage, many techniques can be used to improve the input image for further processing, such as noise reduction or removal, color space transformation, brightness correction, morphological modification, and isolation of the region of interest (roi) (i.e., in this case, the blood spot or part of it as the agglutinated clusters) from the background, among other techniques. in the feature extraction stage, the roi is transformed into a string of descriptors called features (mainly as a number or a sequence of numbers) to be used for identification in the classification stage, where the roi is recognized as agglutination or not. the classification and preprocessing techniques are common between the following studies. in the literature review below, we will concentrate on the feature extraction stage because it represents the fundamental step in the automatic typing process for blood discrimination. ferraz et al. [1] used the standard deviation (sd) calculated from blood images to determine their group. four images are captured for the blood sample using a ccd camera (charge-coupled device) after mixing it with a, b, ab, and d antigens. then, the images are analyzed through a sequence of operations using imaq vision software, which is an image processing software that was developed originally by national instruments. the operations include color conversion and processing, manual thresholding, and morphological operations [1]. the approach was improved in [4], but was tested using only 24 blood samples. the standard deviation feature was also used by talukder et al. [5] and dong et al. [6] after being extracted from the color image and the green band of the blood, respectively. another study used support vector machine (svm) to detect the agglutination based on the standard deviation as well [7]. dhande et al. [8] isolate the blood spot from the background based on its luminance after transforming the rgb (red, green, and blue) input image into an hsv (hue, saturation, and intensity value) color model according to static color values. then, they detect the blobs in the roi and classify them (i.e., into agglutination or not) according to their area. however, these approaches suffer from many limitations such as having manual operations and a relatively long processing time of 2 min. moreover, section 2.2 shows that the standard deviation is not a discriminant feature; it cannot be used alone to discriminate between normal and agglutinated blood spots. furthermore, the usage of the svm by panpatte et al. [7] is not necessary because the classification of the roi based on a single feature (i.e., the standard deviation) can be easily performed using a single threshold value. finally, the approach presented by dhande et al. [8] requires a special environment and configuration for the blood slide, the light intensity, and angle at the time of photo capture because it depends on a static fixed value for the luminance. researchers in [9] detect the contours in the input image and identify the agglutination based on the number of components, where a threshold of 5 is considered for the connected components. however, this technique may result in false blobs in the background. other approaches adopted electrical circuits to perform automatic blood typing. according to the approach in [2], a light is generated by an led and passed through blood samples using optical fiber cables. a specific diode is used as a photo detector. the approach discriminates between different blood groups according to voltage variations from the photo detector. a similar approach was proposed in [3] based on an infrared (ir) light source. the blood sample is located between the ir transmitter and receiver. the blood type is determined according to the intensity of the received ir light. fernandes et al. [10] propose a portable device for blood typing by identifying the spectral differences between agglutinated and nonagglutinated samples, where the result requires up to 5 minutes to be ready. the device was tested using 50 blood samples. a hardware implementation for blood typing system is presented by cruz et al. [11] using a raspberry pi single-board computer. the system detects the contours in the blood spot and determines the existence of the agglutination if the number of the detected contours exceeds a given threshold. a total of 75 blood samples were used in system construction and evaluation. these approaches require additional special hardware. regardless of the additional cost, the hardware may not be available everywhere or every time. many other techniques can be used to classify medical images into normal and infected (i.e., with or without agglutination, in our case). frequency domain analysis was used by yayla et al. [12] to classify nanometer-sized objects in images provided by a biosensor. fourier and wavelet features are extracted from the input images and analyzed where the decision tree and random forest are used for classification. yang et al. [13] proposed a classification method based on the wavelet transformation for feature extraction. the classification is performed using an interpolation scheme. the wavelet decomposition was further improved by proposing an adaptive framework to improve the performance in terms of down sampling balance and signal compression [14]. the wavelet was also used by liu et al. [15] to reduce the haze and enhance texture details of the images in frequency domain. wavelet features can be represented using sophisticated mathematical models such as fractal descriptors [16,17,18]. however, these techniques require domain-dependent knowledge in case of model updating or scaling. although the blood type determination based on feature engineering (i.e., extracting features depending on domain knowledge) is still the most prevalent in research studies, other studies used image matching algorithms such as scale invariant feature transform (sift) and speed-up robust feature (surf). for example, sift was used in [19] to transform the green component of the image into a collection of local feature vectors, after many preprocessing techniques such as thresholding and morphological operations. then, the svm algorithm is used for classification. the proposed approach was evaluated using only 30 blood samples. furthermore, sahastrabuddhe and ajij [20] employed the surf algorithm to detect the agglutination in blood using only 84 blood samples. however, sift and surf algorithms are quite slow compared with other newer image matching algorithms such as oriented fast and rotated brief (orb) [21]. in addition to the limitations discussed for each group of the studies above, all of them used a small number of blood samples and did not provide an objective evaluation of accuracy. as discussed earlier, traditional blood typing is performed manually as follows: (1) taking a blood sample from the patient, (2) mixing it with different antibodies on a slide, (3) observing the agglutination, and (4) determining the blood type. in this paper, we proposed a system to automate the last two steps, (3) and (4). the system can handle a large number of input images captured by a mobile phone camera minimizing human error. such a system has the ability to handle difficult cases that are ambiguous even for manual inspection by people who might fail to detect agglutination and/or blood type while using state-of-the-art approaches. our contribution can be summarized as follows: the orb matching algorithm was adopted to provide an accurate, fast, and automated blood typing system. the system is able to detect various agglutination patterns regardless of variations in photos brightness. the system was evaluated using 1500 images of blood spots that cover all possible agglutination patterns. the evaluation includes detailed analysis for the accuracy and processing time of different approaches. we begin this paper by providing an overview of the blood typing process, problem statement, and challenges, which are explained thoroughly in section 2. the principle of operation of the used image matching techniques is reviewed thoroughly in section 3. the experimental setup, including blood image capturing and analysis, is provided in section 4. results and their analysis are provided in section 5. finally, conclusions are drawn in section 6
الورقات والعروض المنشورة
Letzte Aktualisierung: 2022-07-16
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