scholarly journals Feature extraction on local jet space for texture classification

2015 ◽  
Vol 439 ◽  
pp. 160-170 ◽  
Author(s):  
Marcos William da Silva Oliveira ◽  
Núbia Rosa da Silva ◽  
Antoine Manzanera ◽  
Odemir Martinez Bruno
Keyword(s):  
Feature Extraction ◽  
Texture Classification ◽  
Jet Space

scholarly journals Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

2019 ◽  
Vol 9 (15) ◽  
pp. 3130 ◽  
Author(s):  
Navarro ◽  
Perez
Keyword(s):  
Feature Extraction ◽  
Pattern Classification ◽  
Texture Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Post Processing ◽  
Image Partition ◽  
Global And Local ◽  
Bagging Ensemble ◽  
Color Texture

Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

Texture classification using the rotational-invariant local symmetric tetra pattern

2019 ◽  
Vol 17 (04) ◽  
pp. 1950027
Author(s):  
A. Suruliandi ◽  
A. Sinduja ◽  
S. P. Raja
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Texture Classification ◽  
Texture Feature ◽  
Positive Sign ◽  
Texture Descriptor ◽  
Evaluation Measures ◽  
Local Description ◽  
Rotational Invariant ◽  
Rotationally Invariant

Feature extraction plays a key role in pattern recognition problems. The texture feature is an important feature which helps to describe an image with textural information. A new texture descriptor, the Local Symmetric Tetra Pattern (LSTP), is proposed in this work. This descriptor is developed for the local description of an image. It considers not only the surrounding eight neighbors, but also the eight pixels at the next level to describe the texture efficiently. For every pixel, the maximum edge value, the number of negative sign bits and the number of positive sign bits for each degree of symmetry are computed. Image classification is experimented using the Original Brodatz, Outex and Kylberg Texture Dataset v.1.0 databases. The investigation results are compared with existing method which shows promising achievement of the proposed techniques in terms of their evaluation measures. It is also found that the proposed texture descriptor is rotationally invariant.

scholarly journals Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hong Zhu ◽  
Qianhao Fang ◽  
Yihe Huang ◽  
Kai Xu
Keyword(s):  
Feature Extraction ◽  
Pituitary Tumor ◽  
Sequence Data ◽  
Texture Classification ◽  
Pituitary Tumors ◽  
Image Data ◽  
Extraction Process ◽  
Small Sample ◽  
Image Texture ◽  
Diagnostic Efficiency

Abstract Background Accurately determining the softness level of pituitary tumors preoperatively by using their image textures can provide a basis for surgical options and prognosis. Existing methods for this problem require manual intervention, which could hinder the efficiency and accuracy considerably. Methods We present an automatic method for diagnosing the texture of pituitary tumors using unbalanced sequence image data. Firstly, for the small sample problem in our pituitary tumor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and under-sampled, our method uses a CycleGAN (Cycle-Consistent Adversarial Networks) model for domain conversion to obtain fully sampled MRI spatial sequence. Then, it uses a DenseNet (Densely Connected Convolutional Networks)-ResNet(Deep Residual Networks) based Autoencoder framework to optimize the feature extraction process for pituitary tumor image data. Finally, to take advantage of sequence data, it uses a CRNN (Convolutional Recurrent Neural Network) model to classify pituitary tumors based on their predicted softness levels. Results Experiments show that our method is the best in terms of efficiency and accuracy (91.78%) compared to other methods. Conclusions We propose a semi-supervised method for grading pituitary tumor texture. This method can accurately determine the softness level of pituitary tumors, which provides convenience for surgical selection and prognosis, and improves the diagnostic efficiency of pituitary tumors.

Feature extraction for texture classification

1980 ◽  
Vol 12 (5) ◽  
pp. 301-311 ◽  
Author(s):  
Harry Wechsler ◽  
Todd Citron
Keyword(s):  
Feature Extraction ◽  
Texture Classification

scholarly journals Semi-supervised Method for Image Texture Classification of Pituitary Tumors via CycleGAN and Optimized Feature Extraction

2020 ◽  
Author(s):  
Hong ZHU ◽  
Qianhao FANG ◽  
Yihe HUANG ◽  
Kai XU
Keyword(s):  
Feature Extraction ◽  
Pituitary Tumor ◽  
Sequence Data ◽  
Texture Classification ◽  
Pituitary Tumors ◽  
Image Data ◽  
Extraction Process ◽  
Small Sample ◽  
Image Texture ◽  
Diagnostic Efficiency

Abstract Background: Accurately determining the softness level of pituitary tumors preoperatively by using their image textures can provide a basis for surgical options and prognosis. Existing methods for this problem require manual intervention, which could hinder the efficiency and accuracy considerably. Methods: we present an automatic method for diagnosing the texture of pituitary tumors using unbalanced sequence image data. Firstly, for the small sample problem in our pituitary tumor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and under-sampled, our method uses a CycleGAN model for domain conversion to obtain fully sampled MRI spatial sequence. Then, it uses a DenseNet-ResNet based Autoencoder framework to optimize the feature extraction process for pituitary tumor image data. Finally, to take advantage of sequence data, it uses a CRNN model to classify pituitary tumors based on their predicted softness levels. Results: Experiments show that our method is the best in terms of efficiency and accuracy(91.78%) compared to other methods. Conclusions: We propose a semi-supervised method for grading pituitary tumor texture. This method can accurately determine the softness level of pituitary tumors, which provides convenience for surgical selection and prognosis, and improves the diagnostic efficiency of pituitary tumors.

scholarly journals Semi-supervised Method for Image Texture Classification of Pituitary Tumors via CycleGAN and Optimized Feature Extraction

2019 ◽  
Author(s):  
Hong ZHU ◽  
Qianhao FANG ◽  
Yihe HUANG ◽  
Kai XU
Keyword(s):  
Feature Extraction ◽  
Pituitary Tumor ◽  
Sequence Data ◽  
Texture Classification ◽  
Pituitary Tumors ◽  
Image Data ◽  
Extraction Process ◽  
Small Sample ◽  
Image Texture ◽  
Diagnostic Efficiency

Abstract Background: Accurately determining the softness level of pituitary tumors preoperatively by using their image textures can provide a basis for surgical options and prognosis. Existing methods for this problem require manual intervention, which could hinder the efficiency and accuracy considerably.Methods: we present an automatic method for diagnosing the texture of pituitary tumors using unbalanced sequence image data. Firstly, for the small sample problem in our pituitary tumor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and under-sampled, our method uses a CycleGAN model for domain conversion to obtain fully sampled MRI spatial sequence. Then, it uses a DenseNet-ResNet based Autoencoder framework to optimize the feature extraction process for pituitary tumor image data. Finally, to take advantage of sequence data, it uses a CRNN model to classify pituitary tumors based on their predicted softness levels.Results: Experiments show that our method is the best in terms of efficiency and accuracy(91.78%) compared to other methods.Conclusions: We propose a semi-supervised method for grading pituitary tumor texture. This method can accurately determine the softness level of pituitary tumors, which provides convenience for surgical selection and prognosis, and improves the diagnostic efficiency of pituitary tumors.

Sign in / Sign up

Export Citation Format

Share Document