The image segmentation method we use for leucocytes is based on image distance transformation, combining the region and edge approach, taking full advantage of image information. According to the shape, texture and color appearance of cells, we select 22 feature values and measure them. The classifier is designed on the statistical classification. A test for recognizing 831 leucocytes in 560 images shows that the classification accuracy is 96%. Clinical experts confirm this system; for it can automatically recognize leucocytes by pattern recognition technique, and it is demonstrably valid in practice.
Algorithms ; Artificial Intelligence ; Humans ; Leukocytes ; cytology ; Pattern Recognition, Automated ; methods

In this paper, an image mosaic algorithm based on feature points was proposed by making a study of the image mosaic methods and the characteristics of pathological image. Points of interest were firstly extracted by Moravec operator in this method, and the corresponding feature matches of the original images were achieved by correlation coefficient. The parameters of affine transformation were calculated by the matching feature points, and weighted average method was used to fuse images. The experimental results show that, as for the pathological image with translation and rotation, the algorithm can extract the matching feature points accurately and realize the image seamless mosaic.
Algorithms ; Image Enhancement ; methods ; Image Processing, Computer-Assisted ; methods ; Pathology ; methods ; Pattern Recognition, Automated ; methods

In this paper, some familiar algorithms of edge detection in ultrasound B-scan images are analyzed and studied. The results show that Sobel, Prewitt and Laplacian operators are sensitive to noise, Hough transform adapts to the whole detection, while LoG algorithm's average is zero and it couldn't change the whole dynamic area. Accordingly LoG algorithm is preferable.
Algorithms ; Artifacts ; Humans ; Image Enhancement ; methods ; Pattern Recognition, Automated ; methods ; Ultrasonography ; methods

Digital image processing is widely used in medical image. Segmentation is one of the important step in computer image processing. The essay introduces the procedure and effect of cell image segmentation by watershed algorithm.
Algorithms ; Cytological Techniques ; methods ; Image Processing, Computer-Assisted ; methods ; Pattern Recognition, Automated ; methods

At present, ray-casting algorithm is the most widely used algorithm in the field of medical image visualization, and it can achieve the best image quality. Due to large amounts of computation like sampling, gradient, lighting and blending calculation, the cost of ray-casting algorithm is very large. The characteristic of Streaming single instruction multiple datum extensions (SSE) instruction--supporting vector computation--can satisfy the property of ray-casting algorithm well. Therefore, in this paper, we improved the implementation efficient significantly by vectorization of gradient, lighting and blending calculation, and still achieved a high quality image at the same time.
Algorithms ; Humans ; Image Interpretation, Computer-Assisted ; methods ; Image Processing, Computer-Assisted ; methods ; Pattern Recognition, Automated ; methods

Discrimination of abnormal images from the numerous wireless capsule endoscope (WCE) video sequence images is laborious and time-consuming, so that a computer-based automatic image recognition system is desired for this task. We propose an algorithm to allow feature extraction from each image channel and decision fusion using multiple BP neural networks. The algorithm was tested and the results demonstrated its high efficiency and accuracy in identification of abnormalities in the WCE images.
Algorithms ; Capsule Endoscopy ; methods ; Image Interpretation, Computer-Assisted ; methods ; Pattern Recognition, Automated ; methods

Microscopic characteristics of several Mongolian Herbal flowers were extracted by improved Pseudo-Jacobi (p = 4, q = 2)-Fourier Moments (PJFM's), and 368 different versions of 28 microscopic characteristics of these herbs were identified by using the minimum-mean-distance rule. The experimental results showed that the average identification rate reaches as high as 98.1%. Therefore, this study can provide new techniques for digitalization and visualization of microscopic characteristics of Mongolian Herbs.
China ; Flowers ; ultrastructure ; Image Processing, Computer-Assisted ; methods ; Pattern Recognition, Automated ; methods ; Plants, Medicinal ; ultrastructure

Feature selection (FS) techniques have become an important tool in bioinformatics field. The core algorithm of it is to select the hidden significant data with low-dimension from high-dimensional data space, and thus to analyse the basic built-in rule of the data. The data of bioinformatics fields are always with high-dimension and small samples, so the research of FS algorithm in the bioinformatics fields has great foreground. In this article, we make the interested reader aware of the possibilities of feature selection, provide basic properties of feature selection techniques, and discuss their uses in the sequence analysis, microarray analysis, mass spectra analysis etc. Finally, the current problems and the prospects of feature selection algorithm in the application of bioinformatics is also discussed.
Algorithms ; Artificial Intelligence ; Computational Biology ; methods ; Computer Simulation ; Models, Biological ; Pattern Recognition, Automated ; methods

This paper presents a new method for automatic sleep stage classification which is based on the EEG permutation entropy. The EEG permutation entropy has notable distinction in each stage of sleep and manifests the trend of regular transforming. So it can be used as features of sleep EEG in each stage. Nearest neighbor is employed as the pattern recognition method to classify the stages of sleep. Experiments are conducted on 750 sleep EEG samples and the mean identification rate can be up to 79.6%.
Classification ; methods ; Electroencephalography ; methods ; Entropy ; Humans ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; Sleep Stages ; physiology

Multi-scale principal component analysis based on wavelet transform was applied in feature extraction of sEMG, and bayes classifier was used for pattern classification in this paper. The experiment showed that when Harr wavelet or bior2.6 wavelet was employed to decompose EMG at 5 levels, this method resulted in good performance in the pattern recognition of six movements including varus, ectropion, hand grasps, hand extension, upwards flexion and downwards flexion, with the accuracy of 99.44%. It was superior to the feature extraction based on the statistic feature of wavelet coefficients combined with dimension-reduce by PCA. The research indicated that the proposed method can successfully identify many kinds of movements.
Algorithms ; Electromyography ; methods ; Movement ; Pattern Recognition, Automated ; methods ; Principal Component Analysis ; Signal Processing, Computer-Assisted