Texture analysis method for shape-based segmentation in medical image

2011 ◽  
Author(s):  
Qing Chang ◽  
Bin Zhang ◽  
Ruixiang Liu
Keyword(s):  
Texture Analysis ◽  
Medical Image ◽  
Analysis Method

scholarly journals Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

2020 ◽  
Vol 7 (4) ◽  
pp. 79-86
Author(s):  
Nagadevi Darapureddy ◽  
Nagaprakash Karatapu ◽  
Tirumala Krishna Battula
Keyword(s):  
Image Retrieval ◽  
Texture Analysis ◽  
Medical Image ◽  
Local Binary Pattern ◽  
Medical Images ◽  
Texture Classification ◽  
Medical Image Retrieval ◽  
Local Vector ◽  
Local Derivative ◽  
Local Derivative Pattern

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.

scholarly journals Texture Evolution in Hydroentangled Nonwovens

2003 ◽  
Vol os-12 (1) ◽  
pp. 1558925003os-12 ◽  
Author(s):  
O.B. Berkalp ◽  
B. Pourdeyhimi ◽  
A. Seyam
Keyword(s):  
Texture Analysis ◽  
Preliminary Data ◽  
Texture Evolution ◽  
Process Conditions ◽  
Analysis Method ◽  
Number Of Passes

We recently reported on a co-occurrence-based method for texture analysis. This paper uses this texture analysis method to examine the development of the texture during hydroentangling as a function process conditions. We report on the development of texture as a function of hydroentangling energy. Energy was varied by controlling the pressure and number of passes. All fabrics were produced using the same web on the same forming belt substrate. Our preliminary data indicate that the degree of texture definition increases as a function of pressure up to a point and then begins to deteriorate.

Rice Paper Classification Study Based on Signal Processing and Statistical Methods in Image Texture Analysis

2014 ◽  
Vol 2 (3) ◽  
pp. 1-14
Author(s):  
Haotian Zhai ◽  
Hongbin Huang ◽  
Shaoyan He ◽  
Weiping Liu
Keyword(s):  
Texture Analysis ◽  
Classification Accuracy ◽  
Image Texture ◽  
Analysis Method ◽  
Classification Result ◽  
Image Texture Analysis ◽  
Analysis Methods ◽  
Paper Classification ◽  
Regular Texture ◽  
Better Than

Texture analysis plays an important role in image processing. In the field of texture analysis, the regular texture has been studied a lot, but the natural texture with complex backgrounds is less studied. This paper brings texture analysis into the study of rice paper's classification. First of all it shows the processing flow chart of rice paper classification. By comparing the different kinds of texture analysis methods it chooses the LAWS texture method and uncertainty texture spectrum method to achieve the rice paper classification. When it uses the two texture analysis methods separately, the classification accuracy of rice paper is lower, so it tries to combine the two texture analysis methods. The experimental results show that the classification result got with two combined texture analysis methods is better than that got with one single texture analysis method. The classification accuracy of rice paper has been distinctly improved after the combination of the two texture analysis methods.

Efficient iris texture analysis method based on Gabor ordinal measures

2017 ◽  
Vol 26 (4) ◽  
pp. 043012 ◽  
Author(s):  
Imen Tajouri ◽  
Walid Aydi ◽  
Ahmed Ghorbel ◽  
Nouri Masmoudi
Keyword(s):  
Texture Analysis ◽  
Analysis Method

Relationships of Image Texture Properties with Chewing Activity and Mechanical Properties during Mastication of Bread

2016 ◽  
Vol 12 (4) ◽  
pp. 311-321
Author(s):  
Qian Mao ◽  
Yonghai Sun ◽  
Jumin Hou ◽  
Libo Yu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Texture Analysis ◽  
Texture Features ◽  
Image Texture ◽  
Emg Activity ◽  
Analysis Method ◽  
Texture Properties ◽  
Property Changes ◽  
Occurrence Matrix ◽  
Chewing Activity

Abstract The objective of this study was to investigate the relationships of image texture properties with chewing behaviors, and mechanical properties during mastication of bread. Gray-level gradient co-occurrence matrix (GGCM) was used to process the images of boluses. The chewing behaviors were recorded by electromyography (EMG), and the mechanical properties were measured by texture analyzer. The results showed that among the texture features, the inverse difference moment (IDMGGCM) was selected as the main parameter to describe the decomposition of boluses. IDMGGCM was positively related to the weight gain (r = 0.865, p < 0.01), negatively correlated with hardness (r = –0.835, p <0.01) and EMG activity per cycle (r = –0.767, p < 0.01). GGCM is an effective texture analysis method that could correctly identify 70.1–80.8 % of food bolus images to the corresponding chewing cycles. This study provided a new clue for texture analysis of bread bolus images and offered data revealing the bolus property changes during the mastication of bread.

scholarly journals MEDICINAL PLANT SPECIES IDENTIFICATION SYSTEM USING TEXTURE ANALYSIS AND MEDIAN FILTER

Kursor ◽  
2017 ◽  
pp. 181
Author(s):  
Prihastuti Harsani ◽  
Arie Qurania
Keyword(s):  
Texture Analysis ◽  
Digital Image ◽  
Image Matching ◽  
Web Application ◽  
Median Filter ◽  
Identification System ◽  
Analysis Method ◽  
Plant Species Identification ◽  
Improved Accuracy ◽  
Feature Extraction Technique

Identification of plants can be done through objects - objects in plants by asking an expert or through a specimen (herbarium) that have been identified previously. Identification is done by matching the pictures in the book of flora or monograph. Computer-aided identification can be done using digital image processing methods which utilize digital image matching object plant with a picture on the book. Identification key that is used is the image of the leaves. This study develops previous research has identified using the method of fractal and Euclidian Distance. Accuracy obtained in each of the identification system for the fractal dimension and fractal code is of 68% and 51%. Improved accuracy is the main objective of this study. The proposed method is a method of texture analysis and median filter. Texture analysis is used as feature extraction technique while the median filter is image enhancement techniques. Based on the trials, the results of the identification of texture analysis method and median filter to increase to 78%. Median filter is used as a technique to improve the image quality leaves. The use of an identification system to be tested in the web application of information systems of medicinal plants.

Sign in / Sign up

Export Citation Format

Share Document