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

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

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

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

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

With the utilization of diffusion tensor information of image voxels, a novel MRF (Markov Random Field) segmentation algorithm was proposed for diffusion tensor MRI (DT-MRI) images benefitted from the introduction of Frobenius norm. The comparison of the segmentation effects between the proposed algorithm and K-means segmentation algorithm for DT-MRI image was made, which showed that the new algorithm could segment the DT-MRI images more accurately than the K-means algorithm. Moreover, with the same segmentation algorithm of MRF, better outcomes were achieved in DT-MRI than in conventional MRI (T2WI) image.
Algorithms ; Diffusion Magnetic Resonance Imaging ; methods ; Humans ; Image Interpretation, Computer-Assisted ; methods ; Pattern Recognition, Automated

Usually all heartbeats contribute to the calculation of electrocardiogram (ECG) waveform parameters, such as amplitude and interval, in which those low signal-to-noise-ratio (SNR) heartbeats actually produce a negative effect. In this paper is presented an approach for measuring ECG waveform parameters using template-based methods. Compared with Karhunen-Loeve (KL) template, average template has an advantage over KL template both on speediness and accuracy. Experimental results proved that this approach improved the statistical precision of ECG waveform parameters. In conclusion, this method makes the result of ECG auto analysis more accurate and reliable.
Algorithms ; Electrocardiography ; methods ; Heart Rate ; Humans ; Pattern Recognition, Automated ; methods ; Signal Processing, Computer-Assisted

OBJECTIVETo propose an improved adaptive spectral clustering method for image segmentation to allow automatic selection of the optimal scaling parameters and enhance the accuracy of spectral clustering.

METHODSUsing constrain conditions for optimizing the criterion function and determining the optimal scaling parameters by iteration, the final image segmentation was achieved through spectral clustering based on Nystrom approximation. We chose suit weight functions for different texture images, and used the proposed method for image segmentation. The k-means algorithm and the method of spectral clustering after pre-segmentation by manually choosing the scaling parameter were compared with the proposed method.

RESULTSThe improved spectral clustering algorithm with automatic selection of the optimal scaling parameters achieved better results of image segmentation than the other two methods.

CONCLUSIONThe proposed algorithm can improve the accuracy of spectral clustering for image segmentation.

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