Mouse chromosome classification by radial basis function network with fast orthogonal search

Musavi, M.T.; Bryant, Ruth; Qiao, Maoying; Davisson, Muriel T.; Akeson, Ellen C.; French, Bernie

doi:10.1016/s0893-6080(98)00036-7

Cited by 11 publications

(1 citation statement)

References 15 publications

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“…MUSAVI et al (1998) report a study on the automatic classification of mouse chromosomes from greylevel images using different network architectures. A feedforward NN classifier was trained with feature vectors obtained from grey-scale images.…”

Section: Neural Network Construction and Evaluationmentioning

confidence: 99%

Automatic identification of metaphase spreads and nuclei using neural networks

Cosı́o¹,

Vega²,

Becerra³

et al. 2001

Med. Biol. Eng. Comput.

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The mitotic index (MI) is an important measure in cell proliferation studies. Determination of the MI is usually made by light-microscope analysis of slide preparations. The analyst identifies and counts thousands of cells and reports the percentage of mitotic shapes found among the interphase nuclei. Full automation of this process is an ambitious task, because there can exist very few mitotic shapes among hundreds of nuclei and thousands of artifacts, resulting in a high probability of false positives, i.e. objects erroneously identified as mitosis or nuclei. A semi-automated approach for MI calculation is reported, based on the development of a neural network (NN) for automatic identification of metaphase spreads and stimulated nuclei in digital images of microscope preparations at 10X magnification. After segmentation of the objects on each image, ten different morphometrical, photometrical and textural features are measured on each segmented object. An NN is used to classify the feature vectors into three classes: metaphases, nuclei and artifacts. The system has been able to classify correctly approximately 91% of the objects in each class, in a test set of 191 mitosis, 331 nuclei and 387 artifacts, obtained from 30 different microscope slides. Manual editing of false positives from the metaphase classification results allows the calculation of the MI with an error of 6.5%.

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Section: Neural Network Construction and Evaluationmentioning

confidence: 99%

Automatic identification of metaphase spreads and nuclei using neural networks

Cosı́o¹,

Vega²,

Becerra³

et al. 2001

Med. Biol. Eng. Comput.

View full text Add to dashboard Cite

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Karyotyping of comparative genomic hybridization human metaphases by using support vector machines

2001

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Background:Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method for detecting chromosomal imbalance. Karyotyping of human metaphases is an important step to assign each chromosome to one of 23 or 24 classes (22 autosomes and two sex chromosomes). Automatic karyotyping in CGH analysis is needed. However, conventional karyotyping approaches based on DAPI images require complex image enhancement procedures. Methods: This paper proposes a simple feature extraction method, one that generates density profiles from original true color CGH images and uses normalized profiles as feature vectors without quantization. A classifier is developed by using support vector machine (SVM). It has good generalization ability and needs only limited training samples.

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Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network

Gui

Wang

2005

J Cent. South Univ. Technol.

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Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3 ~, and the model has good generalization ability.

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Mouse chromosome classification by radial basis function network with fast orthogonal search

Cited by 11 publications

References 15 publications

Automatic identification of metaphase spreads and nuclei using neural networks

Automatic identification of metaphase spreads and nuclei using neural networks

Karyotyping of comparative genomic hybridization human metaphases by using support vector machines

Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network

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