Texture based segmentation of cell images using neural networks and mathematical morphology

Martins, Allan de Medeiros; Neto, Adrião Duarte Dória; Brito, Agostinho M.; Sales, Anderson Luis Cavalcanti; Jane, Soo Pei Yi

doi:10.1109/ijcnn.2001.938758

Cited by 1 publication

(3 citation statements)

References 5 publications

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“…The required knowledge usually comes from the application of interest. Many algorithms have been developed specifically for the segmentation of microscopic images of cell cultures [26,28,33,39,40].…”

Section: Segmentationmentioning

confidence: 99%

“…Even though this is the most important step in most image processing operations, it is often the most challenging one. Much work has been devoted to segmentation [2,7,9,10,12,13,15,23,25,[28][29][30]40,42] because of the various kinds of images, the difficulty of the task, the various backgrounds of researchers, and the evolution of computer performances. Hence, there are many classifications of segmentation algorithms.…”

Section: Segmentationmentioning

confidence: 99%

“…In region-based segmentation algorithms [2,7], foreground or background regions are grown based on some tolerance criterion, classified based on histogram analysis [9], or classified based on texture analysis [23,28]. Boundarybased algorithms [30] start with boundary detection followed by region classification.…”

Section: Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Object segmentation, quantification, counting, and tracking

Abu-Tarif¹,

Khiznichenko²,

Northan³

et al. 2004

SPIE Proceedings

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A complete system for object segmentation, counting, quantification, and tracking from microscopic images was implemented. We found that image deconvolution and reconstruction operations are essential to the success of any general-purpose segmentation algorithm and hence are of paramount importance for a counting and tracking software system. Wavelet-based image enhancement, background equalization, and noise suppression routines are the components in our novel general-purpose segmentation algorithm. Simple object recognition based on averages and preset tolerances suffices for most applications. As expected, boundary smoothing is important if watershed-based blob separation is to be used. One of the challenges of a general-purpose counting and tracking system is the need for a large number of object quantification components (features). In tracking we found that incorporating weighted features into an error function improves the accuracy over just the path coherence criterion and that evaluating correspondences over multiple time frames improves the accuracy over using only two consecutive time frames.

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Section: Segmentationmentioning

confidence: 99%