Color and Position versus Texture Features for Endoscopic Polyp Detection

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix.

The method of texture feature extraction and classification of pork loin B ultrasound image is proposed, which can be applied to the computer-aided judgment the pork loin fat content of pork loin. 5 texture features which is based on the texture of the co-occurrence matrix are extracted from the B ultrasound image of pork loin according to the digital image processing algorithm. Using the correlation analysis method to select the key texture extraction in the first step. Then,the classification is realized based on the BP neural network. The train set and test set are randomly chosen from 135 cases. Tests performed show that the proposed method result in a high classification accuracy, which will provide the researcher a valuable opinion on the pork fat content detection.

Extracting efiective features to describe texture is always a key problem in writer identiflcation. This paper proposes a novel method for texture feature extraction by integrating Gabor fllter and Gauss Markov Random Field (GMRF). That is to say, the handwriting images are flrstly flltered by a bank of Gabor fllters in which global features such as directional information can be detected; then GMRF models are developed for every flltered image that is ∞exible enough to capture the local spatial structure and the model parameters of all GMRFs are concatenated as texture features for writer identiflcation, and flnally, Support Vector Machine (SVM) is applied to evaluate the performance of calligraphic artist identiflcation. The experimental results show that this method can achieve a classiflcation rate of 98%, which has outperformed the traditional Gabor fllter method.

A novel method of color and texture feature extraction for image retrieval is proposed. First, an input image is divided into 4×4 nonoverlapping blocks. Then, each block is quantized into two colors by color moment preserving and merging similar colors according to their color difference values under a preset threshold value. A few dominant colors are obtained, and a color histogram of the reconstructed image is computed for the color feature. For the texture feature, by preserving some color moments in the 4×4 neighborhood of each pixel, a bitmap is generated, which can be matched with 64 predefined bitmaps. Then a bitmap spectrum is obtained by counting bitmaps frequencies when all pixels of the input image are considered. An efficient method of similarity measurement is also presented. Experimental results show the proposed method performs well in terms of precision and recall.

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

A new technique is described for observing the structures and mechanisms of suspension feeding in bivalves using endoscopic examination and video image analysis. This method permits direct in vivo observations of whole, intact structures of relatively undisturbed specimens. No surgical alterations of shell or tissue are required for most species. Pallial organ activity can be recorded for future observations and analysis. Using this technique we examined three bivalve species, each with different degrees of mantle fusion:Mya arenaria L.Mytilus edulis L., andPlacopecten magellanicus (Gmelin). The specimens were collected between April and September 1990 at various locations in Trinity Bay, Newfoundland, Canada. Particle retention by the gill and transport of material to the palps was observed, and velocity of particles moving on the gill was determined. We demonstrate that the endoscope-video-analysis system is an efficient and affordable technique suitable for studies of pallial organ function and mechanisms of feeding.

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

This article described the feature extraction methods of crop disease based on computer image processing technology in detail. Based on color, texture and shape feature extraction method in three aspects features and their respective problems were introduced start from the perspective of lesion leaves. Application research of image feature extraction in the filed of crop disease was reviewed in recent years. The results were analyzed that about feature extraction methods, and then the application of image feature extraction techniques in the future detection of crop diseases in the field of intelligent was prospected.

This paper proposes a method for texture feature extraction by integrating Gabor filters and independent component analysis (ICA) for Uyghur handwriting based writer identification. That is, the texture image is firstly filtered by a given bank of Gabor filters, and then higher dimensional feature vectors are constructed from the filtered texture images. Next, the dimensionality of these vectors is reduced by means of principal component analysis (PCA). Finally, the independent components in the resulting vectors with dimensionality reduced are analyzed and extracted by us. Experiments were performed using KNN-5 classifier to Uyghur handwriting samples from 55 different people and promising results of 92.5% correct identification rate were achieved.

The technology for reservoir structure identification has become a challenging problem in the field of imaging logging technology. Because of the huge amount of information and a wide variety, it causes experts with low efficiency on the interpretation of reservoir evaluation and the performance depends highly on the individual experience (including cognitive level, visual decision, etc.). We proposed a new method for texture feature extraction based on macro and micro features. About 3320 imaging logging datasets are fed to support vector machine (SVM) to validate the gains of new method. As a result, the new proposed method achieved an Area Under roc Curve (AUC) value of 0.94.

The aim of this research was to determine an acceptable method of texture feature extraction that could be used for a visual inspection of ceramic tiles quality. Traditional extraction of statistical features has been carried out as well as feature extraction based on local binary pattern operators. For the purpose of testing the proposed methods good and defective ceramic tiles have been identified. The obtained results indicate a possibility for developing a system of a visual inspection of ceramic tiles quality and point out local binary pattern operators as a powerful tool applicable in this particular case.

One of the limitations of the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is a relatively large number of false-positive (FP) detections. Rectal tubes (RTs) are one of the typical sources of FPs because a portion of a RT, especially a portion of a bulbous tip, often exhibits a cap-like shape that closely mimics the appearance of a small polyp. Radiologists can easily recognize and dismiss RT-induced FPs; thus, they may lose their confidence in CAD as an effective tool if the CAD scheme generates such "obvious" FPs due to RTs consistently. In addition, RT-induced FPs may distract radiologists from less common true positives in the rectum. Therefore, removal RT-induced FPs as well as other types of FPs is desirable while maintaining a high sensitivity in the detection of polyps. We developed a three-dimensional (3D) massive-training artificial neural network (MTANN) for distinction between polyps and RTs in 3D CTC volumetric data. The 3D MTANN is a supervised volume-processing technique which is trained with input CTC volumes and the corresponding "teaching" volumes. The teaching volume for a polyp contains a 3D Gaussian distribution, and that for a RT contains zeros for enhancement of polyps and suppression of RTs, respectively. For distinction between polyps and nonpolyps including RTs, a 3D scoring method based on a 3D Gaussian weighting function is applied to the output of the trained 3D MTANN. Our database consisted of CTC examinations of 73 patients, scanned in both supine and prone positions (146 CTC data sets in total), with optical colonoscopy as a reference standard for the presence of polyps. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. These CTC cases were subjected to our previously reported CAD scheme that included centerline-based segmentation of the colon, shape-based detection of polyps, and reduction of FPs by use of a Bayesian neural network based on geometric and texture features. Application of this CAD scheme yielded 96.4% (27/28) by-polyp sensitivity with 3.1 (224/73) FPs per patient, among which 20 FPs were caused by RTs. To eliminate the FPs due to RTs and possibly other normal structures, we trained a 3D MTANN with ten representative polyps and ten RTs, and applied the trained 3D MTANN to the above CAD true- and false-positive detections. In the output volumes of the 3D MTANN, polyps were represented by distributions of bright voxels, whereas RTs and other normal structures partly similar to RTs appeared as darker voxels, indicating the ability of the 3D MTANN to suppress RTs as well as other normal structures effectively. Application of the 3D MTANN to the CAD detections showed that the 3D MTANN eliminated all RT-induced 20 FPs, as well as 53 FPs due to other causes, without removal of any true positives. Overall, the 3D MTANN was able to reduce the FP rate of the CAD scheme from 3.1 to 2.1 FPs per patient (33% reduction), while the original by-polyp sensitivity of 96.4% was maintained.

Wood species identification using feature-level fusion scheme

Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ = 0o, 45o, 90o, 135o). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases.

Automatic segmentation of colorectal polyps from endoscopic images forms an interesting challenge in computer vision. The method proposed in this paper intends to segment colorectal polyp (abnormal) regions from normal regions from a given endoscopic image. Due to lack of any regular texture patterns in this kind of images and apparent visual similarity in background and foreground pixels, conventional texture feature extraction and classification methods do not always yield good results. Hence, active contour based method has been explored to automatically segment out probable abnormal region(s). Our aim is to automatically detect the probable polyp region(s) and then verify the results with respect to the ground truth. Due to lack of very definitive edge criteria along the boundaries of a polyp, we used "active contour without edges" instead of classical active contour.