Classification of red blood cells as normal, sickle, or other abnormal, using a single image analysis feature

Wheeless, Leon L.; Robinson, R. D.; Lapets, Oleg P.; Cox, Christopher; Rubio, Ana; Weintraub, Michael; Benjamin, Lennette J.

doi:10.1002/cyto.990170208

Cited by 41 publications

(35 citation statements)

References 8 publications

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“…Nowadays, automatic classification of erythrocytes using digital images is a very active field of research (Horiuchi et al, 1990;Wheeless et al, 1994;Asakura et al, 1996;Kavitha and Ramakrishnan, 2005;Jayavanth et al, 2010;Frejlichowski, 2010). All these studies are based on the extraction of shape descriptors (either boundary-based or region-based) and the use of Euclidean distance between these descriptors.…”

Section: Supervised Classificationmentioning

confidence: 99%

Geometric Analysis of Planar Shapes With Applications to Cell Deformations

Gual‐Arnau¹,

Herold-García²,

Simó

2015

Image Anal Stereol

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Shape analysis is of great importance in many fields such as computer vision, medical imaging, and computational biology. In this paper we focus on a shape space in which shapes are represented by means of planar closed curves. In this shape space a new metric was recently introduced with the result that this shape space has the property of being isometric to an infinite-dimensional Grassmann manifold of 2-dimensional subspaces. Using this isometry it is possible, from Younes et al. (2008), to explicitly describe geodesics, a task that previously was not at all easy. Our aim is twofold, namely: to use this general theory in order to show some applications to the study of erythrocytes, using digital images of peripheral blood smears, in the treatment of sickle cell disease; and, since normal erythrocytes are almost circular and many Sickle cells have elliptical shape, to particularize the computation of geodesics and distances between shapes using this metric to planar objects considered as deformations of a template (circle or ellipse). The applications considered include: shape interpolation, shape classification, and shape clustering.

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Section: Supervised Classificationmentioning

confidence: 99%

Geometric Analysis of Planar Shapes With Applications to Cell Deformations

Gual‐Arnau¹,

Herold-García²,

Simó

2015

Image Anal Stereol

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“…Automated analysis of RBC morphology from single images has been reported previously, with an emphasis on the diagnosis of sickle cell anemia (11,12,22). These measurements were taken on fixed cells between microscope slides, whereas we measured the morphology of live cells in a microfluidic enclosure.…”

Section: Lifetime Dependence On Cell Shapementioning

confidence: 99%

Alkaline hemolysis fragility is dependent on cell shape: Results from a morphology tracker

et al. 2005

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Background: The morphometric analysis of red blood cells (RBCs) is an important area of study and has been performed previously for fixed samples. We present a novel method for the analysis of morphologic changes of live erythrocytes as a function of time. We use this method to extract information on alkaline hemolysis fragility. Many other toxins lyse cells by membrane poration, which has been studied by averaging over cell populations. However, no quantitative data are available for changes in the morphology of individual cells during membrane poration-driven hemolysis or for the relation between cell shape and fragility. Methods: Hydroxide, a porating agent, was generated in a microfluidic enclosure containing RBCs in suspension. Automatic cell recognition, tracking, and morphometric measurements were done by using a custom image analysis program. Cell area and circular shape factor (CSF) were measured over time for individual cells. Implementations were developed in MATLAB and on Kestrel, a parallel computer that affords higher speed that approaches real-time processing. Results: The average CSF went through a first period of fast increase, corresponding to the conversion of disco-

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“…Aunque los resultados de clasificación fueron excelentes para los drepanocitos, más de la mitad de las células con otras deformaciones fueron clasificadas como normales. Ya con anterioridad, Wheeles, et al 12 habían obtenido resultados similares usando solamente la circularidad de la célula (4πÁrea/ Perímetro 2 ) Oliveros, et al 13 lograron una clasificación semejante en tres clases, pero en vez de células hoz detectaron células elongadas. Poon, et al…”

Section: Introductionunclassified

Clasificación automática de glóbulos rojos en frotis de sangre periférica

Alzate

Rodríguez

2016

revsal

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RESUMENIntroducción: El diagnóstico del estado eritrocitario en frotis de sangre periférica es un proceso realizado normalmente de forma manual a partir de observación microscópica, lo cual implica una considerable inversión de tiempo y recursos, además de posibles problemas de subjetividad y dificultad en la reproducibilidad del diagnóstico. Objetivo: Desarrollar una aplicación que permita la clasificación automática de glóbulos rojos en frotis de sangre periférica, de utilidad como herramienta de ayuda diagnóstica. Metodología: Se usaron técnicas de procesamiento de imágenes para segmentar los eritrocitos en las fotografías microscópicas y medir en ellos área, perímetro, solidez, circularidad, excentricidad, textura y dimensión box-counting. Se usó una red neuronal artificial para clasificar los eritrocitos según sus características en siete clases, incluyendo normalidad y seis alteraciones patológicas. La red se entrenó de acuerdo con la clasificación de 262 eritrocitos realizada por un hematólogo experto. Los desarrollos se hicieron en matlab ® , una poderosa plataforma de computación científica. Resultados: La red escogida alcanza el 97.3% de aciertos en los datos de validación. Las equivocaciones en la red corresponden a células de dudosa clasificación aún para un experto, por presentar características correspondientes a varias clasificaciones patológicas. Conclusiones: La aplicación desarrollada clasifica de manera rápida y acertada los diferentes tipos de glóbulos rojos presentes en una muestra microscópica de frotis de sangre periférica, siendo de utilidad como herramienta de apoyo diagnóstico.Palabras clave: Eritrocito, clasificación eritrocitaria, procesamiento de imágenes, red neuronal, glóbulos rojos. ABSTRACT Introduction:The process of erythrocyte classification in peripheral blood smear is normally done manually from microscopic observation. This implies not only a considerable investment of time and resources but also brings potential problems of subjectivity and difficulty in the reproducibility of diagnosis. Objective: To develop an application that allows the automatic classification of red blood cells in peripheral blood smears, as a diagnostic aid

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Classification of red blood cells as normal, sickle, or other abnormal, using a single image analysis feature

Cited by 41 publications

References 8 publications

Geometric Analysis of Planar Shapes With Applications to Cell Deformations

Geometric Analysis of Planar Shapes With Applications to Cell Deformations

Alkaline hemolysis fragility is dependent on cell shape: Results from a morphology tracker

Clasificación automática de glóbulos rojos en frotis de sangre periférica

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