65 results about "Cell image segmentation" patented technology

An artificial neural network system for image classification, formed of multiple independent individual convolutional neural networks (CNNs), each CNN being configured to process an input image patch to calculate a classification for the center pixel of the patch. The multiple CNNs have different receptive field of views for processing image patches of different sizes centered at the same pixel. A final classification for the center pixel is calculated by combining the classification results from the multiple CNNs. An image patch generator is provided to generate the multiple input image patches of different sizes by cropping them from the original input image. The multiple CNNs have similar configurations, and when training the artificial neural network system, one CNN is trained first, and the learned parameters are transferred to another CNN as initial parameters and the other CNN is further trained. The classification includes three classes, namely background, foreground, and edge.

The invention relates to a cell image segmentation method based on automatic feature learning. As a method for learning features of cell images is very good in feature learning capacity, the cell segmentation accuracy can be greatly improved, and meanwhile, a random forest classifier does not need to select the features, so that the method is capable of well solving the confronted problems of feature extraction and selection in a recognition process. The cell image segmentation method based on the automatic feature learning comprises the following steps: 1, preprocessing: preprocessing initial cell images in a training set and a test set; (2) training a feature extractor; (3) performing recognition by utilizing the random forest classifier; and (4) postprocessing.

The invention discloses a method for segmenting a cervix uteri liquid base cell image. The method comprises the following steps of: 1, cell segmentation: performing illumination correction on an image, filtering noises and enhancing a cell boundary; and dividing the image into three types of cytoplasm, nucleus and background to eliminate a background class area; 2, nucleus segmentation: detecting centers of each of nucleuses in the cell area, estimating the approximate shapes of the nucleuses close to the centers of the nucleuses and correcting the shapes of the nucleuses to acquire an accurate boundary. The method is applied to cervix uteri cells and cell masses in a wide vision range, so that the phenomena of changed illumination, non-uniform dyeing and noise influence to a certain extent are avoided, and the cytoplasm, the single nucleus and adhesion nucleus can be segmented reliably and accurately.

The present invention discloses a thymic cell image segmentation method based on an improved U-Net network, which comprises the following steps: carrying out image preprocessing on a UCSB breast imagedata set; in a U-Net network, adding a void residual module and an attention module; training the U-NET network according to the set training strategy; setting up the evaluation indexes including F1score, object-level Dice coefficient and Hausdorff distance. The optimal model is obtained by optimizing the network through the evaluation indexes. The cell image to be segmented is inputted into theoptimal model, and the segmentation mask is obtained by feature extraction and feature upsampling. By improving a basic segmentation network and creating a new cell image segmentation method, the invention solves the problem of low precision in the automatic segmentation process of the thymus image, and improves the accuracy and efficiency of the segmentation.

The invention discloses a single-cell image segmentation method. The method includes the following steps that: 1) image preprocessing is performed: an image is converted into a grayscale image, noisesare removed, and contrast enhancement is performed; 2) block threshold segmentation is performed so as to segment the image into A*A small blocks, the optimal threshold of each block is calculated byusing the OSTU, so that the foreground and the background of each block can be separated from each other; 3) whether nucleus state features obtained by the previous segmentation step are normal or not is judged, if the nucleus state features are normal, it is proved that a segmentation result is relatively good, and the result is outputted; 4) if the segmentation result does not conform to the nucleus state features, the result is an inaccurate segmentation result, a next step of image processing is performed; and 5) adaptive threshold segmentation is performed, the result of the adaptive threshold segmentation is outputted together with other normal segmented images. With the single-cell image segmentation method of the present invention adopted, the problems of inaccurate nucleus segmentation and slow segmentation speed can be solved. The single-cell image segmentation method combines the advantage of high speed of the block segmentation threshold segmentation and the advantages ofhigh accuracy and low workload of the adaptive threshold segmentation; and since the advantage of the block segmentation threshold segmentation and the advantages of the adaptive threshold segmentation are complementary, the quality of an output image can be improved.

The invention discloses a cell image segmentation method based on a particle swarm neural network. The method comprises following steps of inputting 3*3 windows to replace a traditional single pixel channel; adopting a method based on information entropy to determine the quantity of hidden layer neural elements; selecting activation functions of each layer; simplifying weight training to be an optimization problem; using an improved particle swarm to optimize and solve the problem; carrying out a network training; and analyzing test results. According to the invention, the method is improved based on the traditional BP neural network, so cell images can be well segmented; problems about how to determine the network structure and how to ensure that the network is converged to be overall optimal are solved; segmentation effects of the cell images are improved; and time loss in the cell segmentation process is relatively reduced.

The present invention relates to an image segmentation method and system, and a cell image segmentation method and system based on a corrected gradient image and a watershed algorithm. Since a gradient image of a to-be-segmented image is corrected in advance by a foreground mark obtained by distance transformation and a background mark obtained by watershed transformation and then the corrected gradient image is subjected to watershed transformation to obtain an image segmentation result, not only are the advantages of performing watershed transformation on the gradient image, effectively locating the edges of a target object and segmenting out a complete outline of the target object reserved, but also targets with no obvious boundaries in adhesion areas can be distinguished by the foreground mark and the background mark, the phenomena of under-segmentation and over-segmentation do not occur, and the accuracy of image segmentation is improved. Therefore, the methods and the systems are especially suitable for the field of adhesive and overlapped cell image segmentation.

The invention provides a cell classification method and device, and relates to the technical field of biological information, and the method comprises the steps: obtaining a to-be-identified cell image, wherein the cell image comprises at least two types of cells; dividing the cell image into N sub-images, and determining a contour information graph corresponding to the sub-images, the contour information graph comprising contour shapes of cells to be identified in the sub-images, N being greater than or equal to 2; inputting each contour information graph into a classification model to obtaina classification result of the to-be-identified cells in the sub-image, the classification model being determined according to the contour shape of the cells of which the classification result has been determined; and determining a classification result of the cell image according to the classification result of the to-be-identified cell in the sub-image. The method provided by the embodiment ofthe invention can be used for carrying out cell classification on the whole to-be-identified cell image, is high in cell classification accuracy, is beneficial to carrying out quantity statistics on the classified cells, and improves the diagnosis precision.

The utility model relates to an acquisition and processing system of the cellular morphology image, which aims to construct the image processing platform required by the space cellular embarkation experimental device, comprising a video input processing module, a global logic control module, an image data processing module and an image data transmission module. The video input processing module can convert the analog video signal to digital signal and output the abstracted synchronous signal and pixel clock simultaneously; the global logic control module can realize the logic control of the video input processing and the buffer control of the image data; the image data processing module can complete the acquisition of the cellular morphology image and the cellular image segmentation method based on the edge detection; the image data transmission module can realize the rapid data transmission between the system and the host computer. The utility model has the advantages of integral function, simple structure and flexible programming; in addition, the system can realize the real time online acquisition of the cellular morphology image with high resolution and realize a novel cellular image segmentation method.

The invention relates to a cell image segmentation method and device, computer equipment and a storage medium. The method comprises the following steps: reading a cell grayscale image; carrying out histogram equalization operation on the cell grayscale image to obtain an equalized image; performing morphological operation on the equalized image to obtain a morphological image, wherein the morphological operation comprises top cap operation and gradient operation; detecting cell edges in the morphological image through an edge detection algorithm to obtain a cell edge image; carrying out binarization processing on the cell edge image to obtain a binarized cell image; and carrying out cell segmentation according to the binarized cell image to obtain a cell segmentation image. By adopting themethod, the accuracy of cell image segmentation can be improved.

The invention relates to a cell image semantic segmentation method fusing image segmentation and classification. The method comprises: preprocessing cell image data and then processing the cell imagedata through a bilinear fine-grained classification neural network, an OSTU algorithm and a filling algorithm, and then respectively obtaining a cell classification model and a cell segmentation map;and predicting a foreground connected region of the cell segmentation map by the cell classification model, assigning a prediction result to the connected region so as to obtain a region-by-region classification result, and finally obtaining a semantic segmentation result of the cell test image in combination with a background region obtained by segmentation. A traditional threshold method and a deep learning method are fused to achieve accurate semantic segmentation of the cell image. Compared with a traditional cell image segmentation method, the cell image semantic segmentation method is advantageous in that semantic information of cells can be obtained, the semantic information is of a pixel-by-pixel semantic category, and the method can be applied to identification and isolation of cell contamination.

The embodiment of the invention provides a cell image segmentation method and device. The method comprises the steps of obtaining a cell image to be segmented; inputting the cell image to be segmented into a cell segmentation model to obtain a cell segmentation result outputted by the cell segmentation model, wherein the cell segmentation model is obtained by training an enhanced U-Net network based on a sample cell image and a real label image corresponding to the sample cell image, the enhanced U-Net network is a neural network in which the number of network layers is increased in an initial U-Net network, and a BN layer is added to the network. According to the method and the device provided by the embodiment of the invention, the segmentation effect of the overlapping cells and the adhesion cells is improved based on the initial U-Net network, the cell segmentation accuracy is optimized, while the model training is accelerated based on the BN algorithm, and the model performance is optimized. By inputting the to-be-segmented cell image into the cell segmentation model obtained by training, the rapid, accurate, simple and convenient cell segmentation can be realized.

The invention relates to a particle swarm and fuzzy means clustering based cell image segmentation method. On the basis of an intuitive fuzzy means clustering theory, an intuitive fuzzy membership degree form is improved aiming at cell images and combined with fractional order particle swarms to realize alternate optimization, and a morphological method is adopted for result improvement to realize accurate segmentation of the cell images. The method includes steps: firstly, selecting appropriate parameters, initial particle swarms and relevant data; secondly, starting alternate iterative optimization of the fractional order particle swarms and intuitive fuzzy clustering; thirdly, comparing results with standard fuzzy means clustering results, and selecting a morphological processing method through differential images; finally, subjecting result images to range conversion and watershed segmentation to obtain a final result. The particle swarm and fuzzy means clustering based cell image segmentation method can be widely applied to various cell image based application systems and has a promising market prospect and a high application value.

The invention relates to an adhesion hemocyte image segmentation method based on the improved fractional differential and graph theory.The method comprises the steps of pretreating a hemocyte image by combining morphological denoising with the improved quasi-circular mask operator fractional differential algorithm aiming at the phenomenon that the hemocyte image is fuzzy and low in contrast ratio, wherein by the adoption of the improved fractional differential algorithm, cell edge details are reserved while staining contamination and grain noise of the hemocyte image are filtered out; then conducting preliminary segmentation on the pretreated image with the watershed algorithm, and mapping an over-segmentation area into a node; finally conducting resegmentation on the cell image obtained in the second step with the improved graph theory minimum spanning tree (MST) algorithm.By the adoption of the method, segmentation precision of adherent cells in the cell image can be improved.

The invention discloses a cell image segmentation method based on a Mask contour. The method comprises the following steps of 1, making a data set; 2, cell feature extraction, which comprises the following steps: 2.1, constructing a feature extraction network; 2.2, performing feature multi-scale fusion; 3, establishinga multi-task branch network, mainly establishing a classification branch network, a segmentation branch network and a Center branch network, and respectively sending the fused features into the multi-task branch network for further operation; 4, generating a target Mask contour:generating the Mask contour of an initial target through deformable convolution and a Graham algorithm; and 5, carrying out segmentation Mask contour refinement on the cell image. According to the cell image segmentation method based on the Mask contour provided by the invention, the complexity of an image segmentation task is reduced, the image segmentation processing time is shortened, and the performance is improved.

The invention discloses a cell image segmentation method based on a U-Net network, and relates to the technical field of cell image segmentation. The method comprises the following steps: step 1, an image preprocessing method: 1.1, contrast image enhancement; 1.2, performing Gaussian filtering; 2, a cervical cell segmentation method based on U-Net: 2.1, performing preliminary segmentation by usinga U-Net network; 2.2, adding a hole convolution structure and a residual structure into the U-Net network; 2.3, detecting the edges of the cells by using a clustering detection method; according to the method, a deep learning network is mainly used for segmenting a cervical cell image, and a clustering method is used for auxiliary segmentation; not only can the design difficulty of the classifierbe reduced, but also the accuracy of cancer cell diagnosis can be remarkably improved.

The invention discloses a single-cell hemoglobin determination method and device based on image processing. The method comprises the following steps of: converting a blood sample slide RGB image intoa blood sample slide HSV image, extracting a blood sample slide V channel image, and performing binarization processing on the blood sample slide V channel image according to a set first threshold value to obtain a blood sample slide binarization image; carrying out cell image segmentation processing on the blood sample slide binarization image to extract a single erythrocyte image, and carrying out binarization processing on the single erythrocyte image according to a set second threshold value to remove a central lightly-stained area of erythrocytes; graying the single erythrocyte image of the erythrocyte non-central lightly-stained area to obtain a single erythrocyte gray level image, and calculating the area and hemoglobin content of the whole erythrocyte according to the single erythrocyte gray level image. The method can be adopted to accurately calculate the area of a single erythrocyte and the hemoglobin content.