Recognizing white blood cells with local image descriptors
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Otros documentos de la autoría: López Puigdollers, Dan; Traver Roig, Vicente Javier; Pla, Filiberto
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Recognizing white blood cells with local image descriptorsFecha de publicación
2019-01Editor
ElsevierCita bibliográfica
LÓPEZ PUIGDOLLES, Dan; TRAVER ROIG, Vicente Javier; PLA, Filiberto (2019). Recognizing white blood cells with local image descriptors. Expert Systems with Applications, v. 115, p. 695-708Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S0957417418305396Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Automatic and reliable classification of images of white blood cells is desirable for inexpensive, quick and accurate health diagnosis worldwide. In contrast to previous approaches which tend to rely on image segmen ... [+]
Automatic and reliable classification of images of white blood cells is desirable for inexpensive, quick and accurate health diagnosis worldwide. In contrast to previous approaches which tend to rely on image segmentation and a careful choice of ad hoc
(geometric) features, we explore the possibilities of local image descriptors, since they are a simple approachthey require no explicit
segmentation, and yet they have been shown to be quite robust against background distraction in a number of visual tasks. Despite
its potential, this methodology remains unexplored for this problem. In this work, images are therefore characterized with the
well-known visual bag-of-words approach. Three keypoint detectors and five regular sampling strategies are studied and compared.
The results indicate that the approach is encouraging, and that both the sparse keypoint detectors and the dense regular sampling
strategies can perform reasonably well (mean accuracies of about 80% are obtained), and are competitive to segmentation-based
approaches. Two of the main findings are as follows. First, for sparse points, the detector which localizes keypoints on the cell
contour (oFAST) performs somehow better than the other two (SIFT and CenSurE). Second, interestingly, and partly contrary to our
expectations, the regular sampling strategies including hierarchical spatial information, multi-resolution encoding, or foveal-like
sampling, clearly outperform the two simpler uniform-sampling strategies considered.
From the broader perspective of expert and intelligent systems, the relevance of the proposed approach is that, since it is very
general and problem-agnostic, it makes unnecesary human expertise to be elicited in the form of explicit visual cues; only the labels
of the cell type are required from human domain experts. [-]
Publicado en
Expert Systems with Applications (2019), v. 115Proyecto de investigación
Project P1 · 1B2014-09 of the "Pla de Promoció de la Investigació" from Jaume-I University.Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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