An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depen...An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.展开更多
This paper focuses on the image segmentation with probabilistic neural networks (PNNs). Back propagation neural networks (BpNNs) and multi perceptron neural networks (MLPs) are also considered in this study. Especiall...This paper focuses on the image segmentation with probabilistic neural networks (PNNs). Back propagation neural networks (BpNNs) and multi perceptron neural networks (MLPs) are also considered in this study. Especially, this paper investigates the implementation of PNNs in image segmentation and optimal processing of image segmentation with a PNN. The comparison between image segmentations with PNNs and with other neural networks is given. The experimental results show that PNNs can be successfully applied to image segmentation for good results.展开更多
Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neura...Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neural Networks （CNN） is presented in this paper. It ensures the validity of this scheme and at the same time completes the im-age segmentation faster to accurately calculate the liver volume by using parallel computing in real time. In order to facilitate the CNN irmge process-hag, firstly, three-dimensional liver MRI images should be transformed into binary images; second- ly, an appropriate template parameter of the Global Connectivity Detection CNN （GCD CNN） shall be selected to probe the connectivity of the liver to extract the entire liver; and then the Hole-Filler CNN （HF CNN） are used to repair the entire extracting liver and improve the accuracy of fiver segmentation; final-ly, the liver volume is obtained. Results show that the scheme can ensure the accuracy of the automatic seg-mentation of the liver, and it can also improve the processing speed at the same time. The liver volume calculated is in line with the clinical diagnosis.展开更多
Automatic reading procedures in colon cells biopsies allow a faster and precise reading of microscopic biopsies. These procedures implement automatic image segmentation in order to classify cell types as cancerous or ...Automatic reading procedures in colon cells biopsies allow a faster and precise reading of microscopic biopsies. These procedures implement automatic image segmentation in order to classify cell types as cancerous or noncancerous. The authors have developed a new approach aiming to detect colon cancer cells derived from the ＂Snake＂ method but using a progressive division of the dimensions of the image to achieve rapid segmentation. The aim of the present paper was to classify different cancerous cell types based on nine morphological parameters and on probabilistic neural network. Three types of cells were used to assess the efficiency of our classifications models, including BH （Benign Hyperplasia）, IN （Intraepithelial Neoplasia） that is a precursor state for cancer, and Ca （Carcinoma） that corresponds to abnormal tissue proliferation （cancer）. Results showed that among the nine parameters used to classify cells, only three morphologic parameters （area, Xor convex and solidity） were found to be effective in distinguishing the three types of cells. In addition, classification of unknown cells was possible using this method.展开更多
In this paper, we conduct research on the natural image classification and segmentation algorithm based on GPU and neural network. The application of image segmentation is very broad, almost appeared in all areas rela...In this paper, we conduct research on the natural image classification and segmentation algorithm based on GPU and neural network. The application of image segmentation is very broad, almost appeared in all areas related to image processing, and involved in various types. With the fast development of computing technology and integrated circuit technology, the renewal speed of graphics hardware. Our method combines the GPU with network to optimize the traditional image segmentation and classification methods which will be meaningful. In the future, we will focus our attention on the hardware deployment of the GPU to modify the current approach.展开更多
文摘An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.
文摘This paper focuses on the image segmentation with probabilistic neural networks (PNNs). Back propagation neural networks (BpNNs) and multi perceptron neural networks (MLPs) are also considered in this study. Especially, this paper investigates the implementation of PNNs in image segmentation and optimal processing of image segmentation with a PNN. The comparison between image segmentations with PNNs and with other neural networks is given. The experimental results show that PNNs can be successfully applied to image segmentation for good results.
基金supported by the National Natural Science Foundation of China under Grant No. 61074192the Funds of the USTB under Grants No. YJ2010-019,No.06108104
文摘Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neural Networks （CNN） is presented in this paper. It ensures the validity of this scheme and at the same time completes the im-age segmentation faster to accurately calculate the liver volume by using parallel computing in real time. In order to facilitate the CNN irmge process-hag, firstly, three-dimensional liver MRI images should be transformed into binary images; second- ly, an appropriate template parameter of the Global Connectivity Detection CNN （GCD CNN） shall be selected to probe the connectivity of the liver to extract the entire liver; and then the Hole-Filler CNN （HF CNN） are used to repair the entire extracting liver and improve the accuracy of fiver segmentation; final-ly, the liver volume is obtained. Results show that the scheme can ensure the accuracy of the automatic seg-mentation of the liver, and it can also improve the processing speed at the same time. The liver volume calculated is in line with the clinical diagnosis.
文摘Automatic reading procedures in colon cells biopsies allow a faster and precise reading of microscopic biopsies. These procedures implement automatic image segmentation in order to classify cell types as cancerous or noncancerous. The authors have developed a new approach aiming to detect colon cancer cells derived from the ＂Snake＂ method but using a progressive division of the dimensions of the image to achieve rapid segmentation. The aim of the present paper was to classify different cancerous cell types based on nine morphological parameters and on probabilistic neural network. Three types of cells were used to assess the efficiency of our classifications models, including BH （Benign Hyperplasia）, IN （Intraepithelial Neoplasia） that is a precursor state for cancer, and Ca （Carcinoma） that corresponds to abnormal tissue proliferation （cancer）. Results showed that among the nine parameters used to classify cells, only three morphologic parameters （area, Xor convex and solidity） were found to be effective in distinguishing the three types of cells. In addition, classification of unknown cells was possible using this method.
文摘In this paper, we conduct research on the natural image classification and segmentation algorithm based on GPU and neural network. The application of image segmentation is very broad, almost appeared in all areas related to image processing, and involved in various types. With the fast development of computing technology and integrated circuit technology, the renewal speed of graphics hardware. Our method combines the GPU with network to optimize the traditional image segmentation and classification methods which will be meaningful. In the future, we will focus our attention on the hardware deployment of the GPU to modify the current approach.
