Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network

Cai, Nian; Hu, Kuanghu; Xiong, Hao; Li, Shuyu; Su, Wenjing; Zhu, Fengsui

doi:10.1016/j.patrec.2003.10.009

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…So the method remains very demanding. In order to decrease the computational burden exiting in the traditional MBNN, the technique of preassigning a sufficiently large class number is used in this paper, which results in adaptive determination of the actual class number on the basis of network classification [8]. The MBNN computes the probability of every observed value belonging to every class based on the statistical model of the data.…”

Section: The Architecture Of the Mbnnmentioning

confidence: 99%

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Automatic Analysis of High-Resolution G Bands of Triticum Monococcum Chromosomes Based on the MBNN

Cai¹,

Pan

et al. 2008

2008 Fourth International Conference on Natural Computation

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A method based on the model based neural network (MBNN) is proposed to automatically analyze highresolution G bands of Triticum monococcum chromosomes. The MBNN-3P is employed to segmented the G-band images. Then five features of chromosomes are extracted from the segmented images. At last, the features are input into the MBNN for classification. The results indicate that the method provides a significant way for the automatic analysis of high-resolution G bands of plant chromosomes accurately.

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Section: The Architecture Of the Mbnnmentioning

confidence: 99%

“…In this paper, the MBNN is employed as a core technique to analyze the high-resolution G bands of Triticum monococcum chromosomes. Firstly, the MBNN-3P technique [8] is used to segmented these G bands images. Then some features can be extracted from the images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Analysis of High-Resolution G Bands of Triticum Monococcum Chromosomes Based on the MBNN

Cai¹,

Pan

et al. 2008

2008 Fourth International Conference on Natural Computation

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“…Agam and Dinstein [4] applied the subpixel registration, edge-preserving smoothing nonlinear filter, Kslope evaluation, and an extended chain-code for chromosome segmentation. Cai et al [5] proposed an automatic segmentation of chromosomes base on model-base neural network (MBNN). Grisan et al [1] proposed an automatic procedure of chromosomes segmentation, in which used the variant thresholding scheme and the single-chromosome likelihood (SCL).…”

Section: Introductionmentioning

confidence: 99%

A hybrid technique for overlapped chromosome segmentation of G-band mataspread images automatic

Saiyod

Wayalun

2014

2014 Fourth International Conference on Digital Information and Communication Technology and Its Applications (DICTAP)

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Currently, the cytogenetic disorder such as the Down syndrome and the Prada william syndrome are a serious public health problem. The chromosome G-band images are applied for the automatic Karyotype analysis because it's easy to created and low cost. In contrast, the common problems of the chromosome images are disentangle such as the overlapping chromosome images. In this paper proposed a hybrid method for chromosome segmentation of G-band images. First, the Wayalun method is used to pre-processing the chromosome images. Then the Unsharp Mask and Zhang-Suen method are used searching one node in the overlapping chromosome skeleton images, and the chromosome edge detection is used the chromosome images edge detection algorithm (CEDA). Finally, this method searched the four cut points, in which used to separate the chromosome images overlapping, and the parts of the chromosome segmentation between point to point are created by the linear least squares fit method. The experimental results give the best performance for the automatic chromosome segmentation. A success rate of proposed method achieved at 97%.

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“…For its features, especially the advantage of combining a priori knowledge, the MBNN was attempted to segment the images [5]. The contextual information, however, was not incorporated in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in this paper, we introduce MRF into the MBNN to segment the images and use the term MRF-MBNN for this novel network. To decrease the computation burden, we employ the technique of preassigning a class number [5]. The experimental results show a significant improvement over the MBNN.…”

Section: Introductionmentioning

confidence: 99%

MRF-MBNN: A Novel Neural Network Architecture for Image Processing

Cai

Yang

et al. 2005

Advances in Neural Networks – ISNN 2005

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Abstract. Contextual information and a priori knowledge play important roles in image segmentation based on neural networks. This paper proposed a method for including contextual information in a model-based neural network (MBNN) that has the advantage of combining a priori knowledge. This is achieved by including Markov random field (MRF) into the MBNN and this novel neural network is termed as MRF-MBNN. Then the proposed method is applied to segmenting the images. Experimental results indicate the MRF-MBNN is superior to the MBNN in image segmentation. This study is a successful attempt of incorporating contextual information and a prior knowledge into neural networks to segment images.

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Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network

Cited by 7 publications

References 25 publications

Automatic Analysis of High-Resolution G Bands of Triticum Monococcum Chromosomes Based on the MBNN

Automatic Analysis of High-Resolution G Bands of Triticum Monococcum Chromosomes Based on the MBNN

A hybrid technique for overlapped chromosome segmentation of G-band mataspread images automatic

MRF-MBNN: A Novel Neural Network Architecture for Image Processing

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