Novel Features for Automated Cell Phenotype Image Classification

Subcellular location is related to the knowledge of the spatial distribution of a protein within the cell. The knowledge of the location of all proteins is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. This chapter focuses on the study of machine learning techniques for cell phenotype image classification and is aimed at pointing out some of the advantages of using a multi-classifier system instead of a stand-alone method to solve this difficult classification problem. The main problems and solutions proposed in this field are discussed and a new approach is proposed based on ensemble of neural networks trained by local and global features. Finally, the most used benchmarks for this problem are presented and an experimental comparison among several state-of-the-art approaches is reported which allows to quantify the performance improvement obtained by the approach proposed in this chapter.

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Fast automated cell phenotype image classification

BMC Bioinformatics ◽

10.1186/1471-2105-8-110 ◽

2007 ◽

Vol 8 (1) ◽

Cited By ~ 105

Author(s):

Nicholas A Hamilton ◽

Radosav S Pantelic ◽

Kelly Hanson ◽

Rohan D Teasdale

Keyword(s):

Image Classification ◽

Cell Phenotype

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A reliable method for cell phenotype image classification

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2008.03.005 ◽

2008 ◽

Vol 43 (2) ◽

pp. 87-97 ◽

Cited By ~ 52

Author(s):

Loris Nanni ◽

Alessandra Lumini

Keyword(s):

Image Classification ◽

Reliable Method ◽

Cell Phenotype

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Ensemble of Neural Networks for Automated Cell Phenotype Image Classification

Machine Learning ◽

10.4018/978-1-60960-818-7.ch405 ◽

2012 ◽

pp. 793-816

Author(s):

Loris Nanni ◽

Alessandra Lumini

Keyword(s):

Neural Networks ◽

Image Classification ◽

Subcellular Location ◽

Classification Problem ◽

Machine Learning Techniques ◽

Experimental Comparison ◽

Cell Phenotype ◽

Learning Techniques ◽

Problems And Solutions ◽

Local And Global Features

Subcellular location is related to the knowledge of the spatial distribution of a protein within the cell. The knowledge of the location of all proteins is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. This chapter focuses on the study of machine learning techniques for cell phenotype image classification and is aimed at pointing out some of the advantages of using a multi-classifier system instead of a stand-alone method to solve this difficult classification problem. The main problems and solutions proposed in this field are discussed and a new approach is proposed based on ensemble of neural networks trained by local and global features. Finally, the most used benchmarks for this problem are presented and an experimental comparison among several state-of-the-art approaches is reported which allows to quantify the performance improvement obtained by the approach proposed in this chapter.

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A Novel Method for Cell Phenotype Image Classification

Proceedings of the 3rd International Conference on Electric and Electronics ◽

10.2991/eeic-13.2013.24 ◽

2013 ◽

Cited By ~ 1

Author(s):

Chao Li ◽

Ji-feng Huang

Keyword(s):

Image Classification ◽

Cell Phenotype ◽

Novel Method

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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