DeWAFF: A novel image abstraction approach to improve the performance of a cell tracking system

Calderón, Saúl; Sáenz, Aránzazu; Mora, Rodrigo; Siles, F.; Orozco, Israel Herrera; Buemi, María Elena

doi:10.1109/iwobi.2015.7160148

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Other solutions [6] could be used for noise removal and edge preservation. The deceived bilateral filter allows noise reduction and contrast increase showing the benefit of a good preprocessing phase can overall help the tracking algorithm in its search of the image's cells [7].…”

Section: Related Workmentioning

confidence: 99%

Generación de Datos de Validación para Rastreo Celular en Microscopía de Campo Claro usando Muestras Fluorescentes

Quinde-Cobos

Quirós

Siles

2020

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Este trabajo usa imágenes de fluorescencia de células cancerígenas como para validación de resultados obtenidos por segmentación de imágenes de campo claro de células cancerígenas, ya que actualmente la validación consiste en la anotación manual de las imágenes originales. Se usó reconocimiento de patrones y se inició con preprocesamiento de muestras fluorescentes para asegurar la detección de células, considerando área y valores de intensidad. Al segmentar las imágenes de fluorescencia el núcleo de cada célula es detectado, con su forma original. Al calcular la densidad de cada imagen, estas pueden ser agrupadas de acuerdo a su valor de densidad y usadas para la detección de células en muestras de campo claro.

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Section: Related Workmentioning

confidence: 99%

Generación de Datos de Validación para Rastreo Celular en Microscopía de Campo Claro usando Muestras Fluorescentes

Quinde-Cobos

Quirós

Siles

2020

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“…The parallelized DNLM-MA is done with the concurrent computation of each ∆ iteration. However, since the search window size S is not large enough to provide enough parallelism for outer loop parallelization, we opt to parallelize the computation of ( 4) to (7) to take advantage of the many-core architecture.…”

Section: Previous Parallelization Approaches For Non Local Means Filtermentioning

confidence: 99%

“…We implement and compare different optimizations of Deceived Non Local Means (DNLM) filter [7], which performs both image enhancement and denoising, using the popular edge preserving Non Local Means (NLM) kernel designed for image denoising [8].…”

Section: Introductionmentioning

confidence: 99%

DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

Calderón

Castro

Zurnbado

2018

2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)

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This paper presents a novel computational optimization of the deceived non local means filter using moving average and symmetric weighting. The proposed optimization is compared with different approaches that reduce the computational cost of the deceived non local means filter. Furthermore, the impact of parallelizing different optimization approaches is assessed by evaluating the execution time and scalability in Xeon Phi KNL architecture. The proposed optimization for the sequential implementation achieved a 90x speedup, while its parallelized implementation yielded a speedup of up to 1662x.

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Comparison of Four Automatic Classifiers for Cancer Cell Phenotypes Using M-Phase Features Extracted from Brightfield Microscopy Images

Siles

Mora-Zúñga

Quirós

2020

Communications in Computer and Information Science

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DeWAFF: A novel image abstraction approach to improve the performance of a cell tracking system

Cited by 8 publications

References 32 publications

Generación de Datos de Validación para Rastreo Celular en Microscopía de Campo Claro usando Muestras Fluorescentes

Generación de Datos de Validación para Rastreo Celular en Microscopía de Campo Claro usando Muestras Fluorescentes

DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

Comparison of Four Automatic Classifiers for Cancer Cell Phenotypes Using M-Phase Features Extracted from Brightfield Microscopy Images

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