Red Blood Cells Counting by Circular Hough Transform Using Multispectral Images

Bagui, Olivier K.; Zoueu, Jérémie T.

doi:10.3923/jas.2014.3591.3594

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…Edge detection methods are widely used in segmenting objects from an image. Such methods are used in detecting irregular‐shaped objects, circular objects, and blood cell counting to quote a few. In the second category, an edge detecting system is developed in which edges are detected, meaningfully analyzed, and edges are pruned.…”

Section: Introductionmentioning

confidence: 99%

Edge detection using Chebyshev's orthogonal polynomial and brain extraction from magnetic resonance images of human head

Somasundaram

Kalaividya

Kalaiselvi

et al. 2018

Int J Imaging Syst Tech

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In this article, we propose a new edge detecting method based on the transform coefficients obtained by a point spread function constructed out of Chebyshev's orthogonal polynomials. This edge detector finds edges similar to that of Prewitt and Roberts but is robust against additive and multiplicative noises. We also propose a new scheme to extract brain portion from the magnetic resonance images (MRI) of human head scan by making use of the of the new edge detector. The proposed scheme involves edge detection, morphological operations, and largest connected component analysis. Experiments conducted by applying the proposed scheme on 19 volumes of MRI collected from Internet Brain Segmentation Repository (IBSR) show that the proposed brain extraction scheme performed better than the popular Brain Extraction Tool (BET). The performance of the proposed scheme is measured by computing the Dice coefficient (D) and Jaccard similarity index (J). The proposed method produced a value of 0.9068 for D and 0.8321 for J.

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Section: Introductionmentioning

confidence: 99%

Edge detection using Chebyshev's orthogonal polynomial and brain extraction from magnetic resonance images of human head

Somasundaram

Kalaividya

Kalaiselvi

et al. 2018

Int J Imaging Syst Tech

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show abstract

“…The Hough transform approach only needed a range of cell diameters and the morphology of a cell in an image was divided into circles. This last condition has commonly been observed in blood samples in which the Hough transform approach was successfully used to segment red blood cells in images . In our study, the Hough transform was not only used as a marker of a round‐shaped cell, but also as the summary of cell information to detect a cluster.…”

Section: Resultsmentioning

confidence: 80%

Algorithm for the precise detection of single and cluster cells in microfluidic applications

et al. 2016

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Recent advances in imaging flow cytometry and microfluidic applications have led to the development of suitable mathematical algorithms capable of detecting and identifying targeted cells in images. In contrast to currently existing algorithms, we herein proposed the identification and reconstruction of cell edges based on original approaches that overcome frequent detection limitations such as halos, noise, and droplet boundaries in microfluidic applications. Reconstructed cells are then discriminated between single cells and clusters of round-shaped cells, and cell information such as the area and location of a cell in an image is output. Using this method, 76% of cells detected in an image had an error <5% of the cell area size and 41% of the image had an error <1% of the cell area size (n 5 1,000). The method developed in the present study is the first image processing algorithm designed to be flexible in use (i.e. independent of the size of an image, using a microfluidic droplet system or not, and able to recognize cell clusters in an image) and provides the scientific community with a very accurate imaging algorithm in the field of microfluidic applications. V C 2016 International Society for Advancement of CytometryKey terms algorithm; microfluidic; cell detection; cell reconstruction; imaging processing; droplet THE morphological identification of a cell offers numerous advantages when discriminating elements such as cell populations based on their shapes. However, this time-consuming method is limited to the skills of the operator and is associated with difficulty in imaging cell sorters where quick decisions must be made based on the information contained in each image. The combination of a high speed camera and algorithm has been proposed in order to routinely and rapidly measure the concentration of target cells in a sample. This recent imaging cytometric approach associated with microfluidic devices indicates that the automatic detection of a cell in an image is applicable to the identification of rare cell types (e.g., possible circulating tumor cells) without any fluorescence label (1). In this context, the morphological properties of a cell such as size area, diameter, and circularity must be calculated with the highest precision in order to limit inaccurate diagnoses.In the image processing field, numerous algorithms have been created and improved with the aim of correctly identifying the edge of an object and providing reliable data in greyscale and color images (2-7). However, in imaging flow cytometric systems and microfluidic devices, cells moving in the flow may hamper these common edge detectors and led to the incomplete detection of cell walls. To overcome this limitation and improve the detection of a cell in an image, several specific algorithms have been developed in the past decade (8-11). Although these algorithms provided interesting findings, some common limitations remained. In point of view of the flexibility in use, development of specific algorithms leads to focus on ca...

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“…Multispectral microscopy is a technique that exploits spectral and spatial information resulting from lightmatter interaction. Several studies have reported the use of multispectral microscopy in the detection of malaria without fluorescent labelling of cells 10,11 and the early detection of plant diseases. 12,13 However, there is a need to improve spatial resolution and noise performance in these systems.…”

Section: Introductionmentioning

confidence: 99%

Unstained blood smear contrast enhancement using spectral time multiplexing super resolution

Yebouet

Diby

Kaduki

et al. 2020

J. Spectral Imaging

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We report the use of Time Multiplexing Super Resolution (TMSR) to reduce significantly speckle noise in spectral imaging microscopy of unstained thin blood smear samples of malaria-infected blood. The method is based on combining speckle illumination with a moving array serving as an encoding mask. We propose the use of a new encoding mask to improve the performance of the conventional TMSR method. The new mask is a two-dimensional generalisation of the one- dimensional Ipatov code. The mask is projected on the object and 13 low-resolution images captured and subsequently decoded properly using the same array. The low contrast images are added and extracted from the resulting reconstruction, giving a super-resolved, high-contrast image. The Ipatov filter used in this work performs better than the Barker filter.

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Red Blood Cells Counting by Circular Hough Transform Using Multispectral Images

Cited by 11 publications

References 4 publications

Edge detection using Chebyshev's orthogonal polynomial and brain extraction from magnetic resonance images of human head

Edge detection using Chebyshev's orthogonal polynomial and brain extraction from magnetic resonance images of human head

Algorithm for the precise detection of single and cluster cells in microfluidic applications

Unstained blood smear contrast enhancement using spectral time multiplexing super resolution

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