Leopard Image Retrieval using Region of Interest and Texture Feature

2006 ◽  
Author(s):  
Jing Zhang ◽  
Seok-Wun Ha
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Region Of Interest

scholarly journals Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors

2021 ◽  
Author(s):  
Salvatore Gitto ◽  
Renato Cuocolo ◽  
Ilaria Emili ◽  
Laura Tofanelli ◽  
Vito Chianca ◽  
...  
Keyword(s):  
Bone Tumors ◽  
Intraclass Correlation ◽  
Texture Feature ◽  
Region Of Interest ◽  
Unenhanced Ct ◽  
Unenhanced Computed Tomography ◽  
2D And 3D ◽  
Feature Stability ◽  
Ct And Mri ◽  
T2 Weighted Images

AbstractThis study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied to both 2D and 3D segmentations to evaluate the influence of segmentation margins. A total of 783 and 1132 features were extracted from original and filtered 2D and 3D images, respectively. Intraclass correlation coefficient ≥ 0.75 defined feature stability. In 2D vs. 3D contour-focused segmentation, the rates of stable features were 74.71% vs. 86.57% (p < 0.001), 77.14% vs. 80.04% (p = 0.142), and 95.66% vs. 94.97% (p = 0.554) for CT and T1-weighted and T2-weighted images, respectively. Margin shrinkage did not improve 2D (p = 0.343) and performed worse than 3D (p < 0.001) contour-focused segmentation in terms of feature stability. In 2D vs. 3D contour-focused segmentation, matching stable features derived from CT and MRI were 65.8% vs. 68.7% (p = 0.191), and those derived from T1-weighted and T2-weighted images were 76.0% vs. 78.2% (p = 0.285). 2D and 3D radiomic features of cartilaginous bone tumors extracted from unenhanced CT and MRI are reproducible, although some degree of interobserver segmentation variability highlights the need for reliability analysis in future studies.

scholarly journals Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

A NEW METHOD OF MEDICAL IMAGE RETRIEVAL BASED ON COLOR–TEXTURE CORRELOGRAM AND GTI MODEL

2009 ◽  
Vol 08 (02) ◽  
pp. 239-248 ◽  
Author(s):  
XIAO-YING TAI ◽  
LI-DONG WANG ◽  
QIN CHEN ◽  
REN FUJI ◽  
KITA KENJI
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Feature Vector ◽  
Texture Feature ◽  
Image Feature ◽  
Similarity Metric ◽  
Medical Image Retrieval ◽  
Endoscopic Image ◽  
Color Correlogram ◽  
Color Texture

This paper presents a method for endoscopic image retrieval based on color–texture correlogram and Generalized Tversky's Index (GTI) model. First we define a new image feature named color–texture correlogram, which is the extension of color correlogram. The texture image extracted by texture spectrum algorithm is combined with color feature vector, and then we calculate the spatial correlation of color–texture feature vector. Similarity metric is also the key technology during domain of image retrieval, GTI model is used in medical image retrieval for similarity metric, and the technique of relevance feedback is used in the algorithm to enhance the efficiency of retrieval. Experimental results show that the method discussed in this paper is much more effective.

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