This paper is a study on texture analysis of Computer Tomography (CT) liver images using orthogonal moment features. Orthogonal moments are used as image feature representation in many applications like invariant patt...This paper is a study on texture analysis of Computer Tomography (CT) liver images using orthogonal moment features. Orthogonal moments are used as image feature representation in many applications like invariant pattern recognition of images. Orthogonal moments are proposed here for the diagnosis of any abnormalities on the CT images. The objective of the proposed work is to carry out the comparative study of the performance of orthogonal moments like Zernike, Racah and Legendre moments for the detection of abnormal tissue on CT liver images. The Region of Interest (ROI) based segmentation and watershed segmentation are applied to the input image and the features are extracted with the orthogonal moments and analyses are made with the combination of orthogonal moment with segmentation that provides better accuracy while detecting the tumor. This computational model is tested with many inputs and the performance of the orthogonal moments with segmentation for the texture analysis of CT scan images is computed and compared.展开更多
The non-destructive testing of brazed joint in honeycomb structure with thin panel （ thickness ： 0. 2 mm） was studied by ultrasonic C-scan method. Samples with different types of artificial defect were designed; th...The non-destructive testing of brazed joint in honeycomb structure with thin panel （ thickness ： 0. 2 mm） was studied by ultrasonic C-scan method. Samples with different types of artificial defect were designed; the characteristic signal and the main parameters of the test were determined by the pre-experiment, and then parameters were optimized by orthogonal design, finally the optimum process was verified by a single panel sample. The multiple reflection echoes were chosen as the characteristic signal. The optimal C-scan results were achieved when the 20 MHz focus probe was used, and the pass band range for received signal were selected as 8 - 17. 5 MHz. The defects such as incomplete penetration and core damage can be detected with ultrasonic C-scan, and the detection accuracy can reach to 1 ram.展开更多
文摘This paper is a study on texture analysis of Computer Tomography (CT) liver images using orthogonal moment features. Orthogonal moments are used as image feature representation in many applications like invariant pattern recognition of images. Orthogonal moments are proposed here for the diagnosis of any abnormalities on the CT images. The objective of the proposed work is to carry out the comparative study of the performance of orthogonal moments like Zernike, Racah and Legendre moments for the detection of abnormal tissue on CT liver images. The Region of Interest (ROI) based segmentation and watershed segmentation are applied to the input image and the features are extracted with the orthogonal moments and analyses are made with the combination of orthogonal moment with segmentation that provides better accuracy while detecting the tumor. This computational model is tested with many inputs and the performance of the orthogonal moments with segmentation for the texture analysis of CT scan images is computed and compared.
文摘The non-destructive testing of brazed joint in honeycomb structure with thin panel （ thickness ： 0. 2 mm） was studied by ultrasonic C-scan method. Samples with different types of artificial defect were designed; the characteristic signal and the main parameters of the test were determined by the pre-experiment, and then parameters were optimized by orthogonal design, finally the optimum process was verified by a single panel sample. The multiple reflection echoes were chosen as the characteristic signal. The optimal C-scan results were achieved when the 20 MHz focus probe was used, and the pass band range for received signal were selected as 8 - 17. 5 MHz. The defects such as incomplete penetration and core damage can be detected with ultrasonic C-scan, and the detection accuracy can reach to 1 ram.
