Med Biol Eng Comput - Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images.

Tópicos

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{ featur(3375) classif(2383) classifi(1994) }
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Resumo

Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-prone. We propose a user interaction-independent framework that segments cellular regions, splits the overlapping cells and counts the total number of single neuroblastic cells. Our novel segmentation algorithm regards an image as a feature space constructed by joint spatial-intensity features of color pixels. It clusters the pixels within the feature space using mean-shift and then partitions the image into multiple tiles. We propose a novel color analysis approach to select the tiles with similar intensity to the cellular regions. The selected tiles contain a mixture of single and overlapping cells. We therefore also propose a cell counting method to analyse morphology of the cells and discriminate between overlapping and single cells. Ultimately, we apply watershed to split overlapping cells. The results have been evaluated by a pathologist. Our segmentation algorithm was compared against adaptive thresholding. Our cell counting algorithm was compared with two state of the art algorithms. The overall cell counting accuracy of the system is 87.65?%.

Resumo Limpo

neuroblastoma malign tumor cancer childhood deriv neural crest number neuroblast cell within tumor provid signific prognost inform pathologist enorm number neuroblast cell make process count tedious errorpron propos user interactionindepend framework segment cellular region split overlap cell count total number singl neuroblast cell novel segment algorithm regard imag featur space construct joint spatialintens featur color pixel cluster pixel within featur space use meanshift partit imag multipl tile propos novel color analysi approach select tile similar intens cellular region select tile contain mixtur singl overlap cell therefor also propos cell count method analys morpholog cell discrimin overlap singl cell ultim appli watersh split overlap cell result evalu pathologist segment algorithm compar adapt threshold cell count algorithm compar two state art algorithm overal cell count accuraci system

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