Int J Comput Assist Radiol Surg - Using the low-pass monogenic signal framework for cell/background classification on multiple cell lines in bright-field microscope images.

Tópicos

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Resumo

RPOSE: Several cell detection approaches which deal with bright-field microscope images utilize defocusing to increase image contrast. The latter is related to the physical light phase through the transport of intensity equation (TIE). Recently, it was shown that it is possible to approximate the solution of the TIE using a low-pass monogenic signal framework. The purpose of this paper is to show that using the local phase of the aforementioned monogenic signal instead of the defocused image improves the cell/background classification accuracy.MATERIALS AND METHODS: The paper statement was tested on an image database composed of three cell lines: adherent CHO, adherent L929, and Sf21 in suspension. Local phase and local energy images were generated using the low-pass monogenic signal framework with axial derivative images as input. Machine learning was then employed to investigate the discriminative power of the local phase. Three classifier models were utilized: random forest (RF), support vector machine (SVM) with a linear kernel, and SVM with a radial basis function (RBF) kernel.RESULTS: The improvement, averaged over cell lines, of classifying 5?5 sized patches extracted from the local phase image instead of the defocused image was 7.3% using the RF, 11.6% using the linear SVM, and 10.2% when a RBF kernel was employed instead of the linear one. Furthermore, the feature images can be sorted by increasing discriminative power as follows: at-focus signal, local energy, defocused signal, local phase. The only exception to this order was the superiority of local energy over defocused signal for suspended cells.CONCLUSIONS: Local phase computed using the low-pass monogenic signal framework considerably outperforms the defocused image for the purpose of pixel-patch cell/background classification in bright-field microscopy.

Resumo Limpo

rpose sever cell detect approach deal brightfield microscop imag util defocus increas imag contrast latter relat physic light phase transport intens equat tie recent shown possibl approxim solut tie use lowpass monogen signal framework purpos paper show use local phase aforement monogen signal instead defocus imag improv cellbackground classif accuracymateri method paper statement test imag databas compos three cell line adher cho adher l sf suspens local phase local energi imag generat use lowpass monogen signal framework axial deriv imag input machin learn employ investig discrimin power local phase three classifi model util random forest rf support vector machin svm linear kernel svm radial basi function rbf kernelresult improv averag cell line classifi size patch extract local phase imag instead defocus imag use rf use linear svm rbf kernel employ instead linear one furthermor featur imag can sort increas discrimin power follow atfocus signal local energi defocus signal local phase except order superior local energi defocus signal suspend cellsconclus local phase comput use lowpass monogen signal framework consider outperform defocus imag purpos pixelpatch cellbackground classif brightfield microscopi

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