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Recognition of fracture surface images based on fuzzy gray level co-occurrence matrix and hidden Markov model
基于模糊灰度共生矩阵与隐马尔可夫模型的断口图像识别

liling,Li Ming,LU Yuming,
李凌
,黎明,鲁宇明

中国图象图形学报 , 2010,
Abstract: Texture is usually depicted by a gray-level distribution along with a certain spatial interaction. Gray level co-occurrence matrix(GLCM) is an appropriate candidate to depicted texture because of its capability of blending spatial interaction with gray-level distribution, thus, it can be widely applied in texture analysis. When calculating GLCM, the gray-level quantization would be needed in order to decrease matrix dimension, and certain information would be lose. A membership function matrix is established whereby the distance which between the real gray-level and the mean of quantization gray-levels area, and then, a newly co-occurrence matrix, namely fuzzy gray level co-occurrence matrix(FGLCM) is proposed. After appropriate features are selected based on FGLCM statistics properties analysis, the hidden markov model(HMM) classification is applied to divide the classical fracture surface image to four kinds. It is proved practically that FGLCM in this paper is better than the GLCM in depicting textures and the FGLCM combined with HMM is efficient performance in fracture surface images classification, and the recognition rate is 98%.
New Edge Detection Technique based on the Shannon Entropy in Gray Level Images
Mohamed A. El-Sayed,,Tarek Abd-El Hafeez
International Journal on Computer Science and Engineering , 2011,
Abstract: Edge detection is an important field in image processing. Edges characterize object boundaries and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. In this paper, an approach utilizing an improvement of Baljit and Amar method which uses Shannon entropy otherthan the evaluation of derivates of the image in detecting edges in gray level images has been proposed. The proposed method can reduce the CPU time required for the edge detection process and the quality of the edge detector of the output images is robust. A standard test images, the real-world and synthetic images are used to compare the results of the proposed edge detector with the Baljit and Amar edge detector method. In order to validate the results, the run time of the proposed method and the pervious method are presented. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The performance evaluation of the proposed technique in terms of the measured CPU time and the quality of edge detector method are presented. Experimental results demonstrate that the proposed method achieve better result than the relevant classic method.
New Edge Detection Technique based on the Shannon Entropy in Gray Level Images  [PDF]
Mohamed A. El-Sayed,Tarek Abd-El Hafeez
Computer Science , 2012,
Abstract: Edge detection is an important field in image processing. Edges characterize object boundaries and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. In this paper, an approach utilizing an improvement of Baljit and Amar method which uses Shannon entropy other than the evaluation of derivatives of the image in detecting edges in gray level images has been proposed. The proposed method can reduce the CPU time required for the edge detection process and the quality of the edge detector of the output images is robust. A standard test images, the real-world and synthetic images are used to compare the results of the proposed edge detector with the Baljit and Amar edge detector method. In order to validate the results, the run time of the proposed method and the pervious method are presented. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The performance evaluation of the proposed technique in terms of the measured CPU time and the quality of edge detector method are presented. Experimental results demonstrate that the proposed method achieve better result than the relevant classic method.
Breast Ultrasound Images Enhancement Using Gray Level Co-Occurrence Matrices Quantizing Technique
International Journal of Information Science , 2012, DOI: 10.5923/j.ijis.20120205.02
Abstract: This article demonstrates a simple and robust enhancement method for breast ultrasound images based on quantizing the gray level intensities. The quantizing is performed using gray level co-occurrence matrices; that calculates the neighbor intensity interrelation according to the number of gray intensities per level. In this research we divide the gray scale to 15 levels. Gaussian and median filtrations were implemented and iterated 7 times, at each level, using a kernel size of 11x11. Finally each filtered level is translated back to its original location. This quantization technique significantly smoothes the breast ultrasound image while preserving edges. The performance of the algorithm has been compared with the standard filtering technique and evaluated using second order statistical methods. Test and synthesized images with induced speckle noise were used for technique verification and automatic edge detection. The proposed method demonstrates high filtration quality performance and edge preservation compared to the standard overall image filtration method. The textures were preserved with slight blurring. The proposed method introduces a new enhancing technique based on second order dependency matrices quantizing technique.
A New Image Steganography Approach for Information Security Using Gray Level Images in Spatial Domain  [PDF]
Rajkumar Yadav,,Ravi Saini,Kamaldeep
International Journal on Computer Science and Engineering , 2011,
Abstract: A new image steganography method for hiding data using Gray Level Images in Spatial Domain is proposed in this paper. This method uses the 5th, 6th and 7th bits of pixel value for insertion and retrieval of message by using the same bits of pixel value. This method is an improvement over earlier methods like least significant bit (LSB) method [4] and gray level modification (GLM) method [6]. This method retains the advantages of above said methods but discards the disadvantages associated with above methods and provides us the better results.
Prototype System for Retrieval of Remote Sensing Images based on Color Moment and Gray Level Co-Occurrence Matrix  [PDF]
Priti Maheshwary,Namita Srivastava
International Journal of Computer Science Issues , 2009,
Abstract: The remote sensing image archive is increasing day by day. The storage, organization and retrieval of these images poses a challenge to the scienitific community. In this paper we have developed a system for retrieval of remote sensing images on the basis of color moment and gray level co-occurrence matrix feature extractor. The results obtained through prototype system is encouraging.
3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images  [PDF]
Oranit Boonsiri, Kiyotada Washiya, Kota Aoki, Hiroshi Nagahashi
Journal of Biosciences and Medicines (JBM) , 2016, DOI: 10.4236/jbm.2016.43009
Abstract:

Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.

SD LMS L-Filters for Filtration of Gray Level Images in Timespatial Domain Based on GLCM Features  [cached]
Robert Hudec,Miroslav Benco,Janko Krajcovic
Advances in Electrical and Electronic Engineering , 2008,
Abstract: In this paper, the new kind of adaptive signal-dependent LMS L-filter for suppression of a mixed noise in greyscale images is developed. It is based on the texture parameter measurement as modification of spatial impulse detector structure. Moreover, the one of GLCM (Gray Level Co-occurrence Matrix) features, namely, the contrast or inertia adjusted by threshold as switch between partial filters is utilised. Finally, at the positions of partial filters the adaptive LMS versions of L-filters are chosen.
Steganalysis of LSB Embedded Images Using Gray Level Co-Occurrence Matrix
H.B.Kekre, A.A.Athawale, Sayli Anand Patki
International Journal of Image Processing , 2011,
Abstract: This paper proposes a steganalysis technique for both grayscale and colorimages. It uses the feature vectors derived from gray level co-occurrence matrix(GLCM) in spatial domain, which is sensitive to data embedding process. ThisGLCM matrix is derived from an image. Several combinations of diagonalelements of GLCM are considered as features. There is difference between thefeatures of stego and non-stego images and this characteristic is used forsteganalysis. Distance measures like Absolute distance and Euclidean distanceare used for classification. Experimental results demonstrate that the proposedscheme outperforms the existing steganalysis techniques in attacking LSBsteganographic schemes applied to spatial domain.
Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features
A.PADMA,R.SUKANESH
International Journal of Advanced Computer Sciences and Applications , 2011,
Abstract: Tumor classification and segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. This paper deals with an efficient segmentation algorithm for extracting the brain tumors in computed tomography images using Support Vector Machine classifier. The objective of this work is to compare the dominant grey level run length feature extraction method with wavelet based texture feature extraction method and SGLDM method. A dominant gray level run length texture feature set is derived from the region of interest (ROI) of the image to be selected. The optimal texture features are selected using Genetic Algorithm. The selected optimal run length texture features are fed to the Support Vector Machine classifier (SVM) to classify and segment the tumor from brain CT images. The method is applied on real data of CT images of 120 images with normal and abnormal tumor images. The results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of classification accuracy. From the analysis and performance measures like classification accuracy, it is inferred that the brain tumor classification and segmentation is best done using SVM with dominant run length feature extraction method than SVM with wavelet based texture feature extraction method and SVM with SGLDM method. In this work,we have attempted to improve the computing efficiency as it selects the most suitable feature extration method that can used for classification and segmentation of brain tumor in CT images efficiently and accurately. An avearage accuracy rate of above 97% was obtained usinh this classification and segmentation algorithm.
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