On the influence of the image normalization scheme on texture classification accuracy

2018 ◽  
Author(s):  
Marcin Kociolek ◽  
Michal Strzelecki ◽  
Szvmon Szymajda
Keyword(s):  
Classification Accuracy ◽  
Texture Classification ◽  
Image Normalization ◽  
Normalization Scheme

scholarly journals Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 286
Author(s):  
Clément Acquitter ◽  
Lucie Piram ◽  
Umberto Sabatini ◽  
Julia Gilhodes ◽  
Elizabeth Moyal Cohen-Jonathan ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Predictive Power ◽  
Clinical Observation ◽  
Predictive Performance ◽  
Multiparametric Mri ◽  
Random Forest Classifier ◽  
Additional Benefit ◽  
Image Normalization ◽  
Model Based

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.

Texture Classification Based on Empirical Wavelet Transform Using LBP Features

2017 ◽  
Vol 8 (3) ◽  
pp. 623
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Automatic Inspection ◽  
Gray Scale ◽  
Novel Approach ◽  
Empirical Wavelet Transform ◽  
Inspection Systems

<p>Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.</p>

COMPUTING IMAGE TEXTURE BY ISOPATTERN

2014 ◽  
Vol 28 (05) ◽  
pp. 1454003
Author(s):  
S. MOHAMED MANSOOR ROOMI ◽  
R. RAJA ◽  
D. KALAIYARASI
Keyword(s):  
Texture Analysis ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Weighting Function ◽  
Texture Classification ◽  
Texture Feature ◽  
Region Of Interest ◽  
Image Texture ◽  
Textural Feature ◽  
Bit Plane

Texture is an important feature that aids in identifying objects of interest or region of interest irrespective of the source of the image. In this paper, a novel and simple isopattern-based texture feature is introduced. Spatial gray scale dependencies represented by bit plane is analyzed for specific patterns and are accumulated in bins. These are scaled by half-normal weighting function to provide isopattern texture feature. The ability of this texture feature in capturing textural variations of the images despite the presence of illumination, scale and rotation is demonstrated by conducting texture analysis on Brodatz, OuTex texture datasets and its classification accuracy on Kylberg dataset. The results of these two experimentation indicate that the proposed textural feature picks variation in texture significantly and has a better texture classification accuracy of 98.26% when compared with the state-of-the-art features like Gabor, GLCM and LBP.

scholarly journals Optimal filter bank representation in texture classification using support vector machines

2021 ◽  
Author(s):  
Samsher Singh Sidhu
Keyword(s):  
Support Vector Machines ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Texture Classification ◽  
Classification Method ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Translation Invariant ◽  
Vector Machines ◽  
Wavelet Frame Transform

Texture analysis has been a field of study for over three decades in many fields including electrical engineering. Today, texture analysis plays a crucial role in many tasks ranging from remote sensing to medical imaging. Researchers in this field have dealt with many different approaches, all trying to achieve the goal of high classification accuracy. The main difficulty of texture analysis was the lack of ability of the tools to characterize adequately different scales of the textures effectively. The development in multi-resolution analysis such as Gabor and Wavelet Transform help to overcome this difficulty. This thesis describes the texture classification algorithm that uses the combination of statistical features and co-occurrence features of the Discrete Wavelet Transformed images. The classification accuracy is increased by using translation-invariant features generated from the Discrete Wavelet Frame Transform. The results are further improved by focussing on the transformed images used for feature extraction by using filters which essentially extract those areas of the image that discriminate themselves from other image classes. In effect, by reducing the spatial characteristics of images that contribute to the features, the texture classification method still has the ability to preserve the classification accuracy. Support Vector Machines has proved excellent performance in the area of pattern recognition problems. We have applied SVMs with the texture classification method described above and, when compared to traditional classifiers, SVM has produced more accurate classification results on the Brodatz texture album.

scholarly journals Classification of Natural Textures using Rule Based Rank Matrix

2020 ◽  
Vol 9 (3) ◽  
pp. 2660-2666
Keyword(s):  
Classification Accuracy ◽  
Texture Classification ◽  
Experimental Results ◽  
Texture Image ◽  
Rule Based ◽  
Rank Matrix ◽  
Frame Work ◽  
Rank Vector ◽  
State Of Art

This paper derives a new frame work for the classification of natural textures based on gradient rank vectors derived on a 2 X 2 grid. This paper identified the ambiguity in deriving ranks when two or more positions of the grid possess the same value. To attend this ambiguity and without increasing the total number of rank vectors on d positions this paper derived a rule based rank vector frame work. This paper replaced the 2 X 2 grid with the column position of the Rule based Rank Word Matrix (RRWM). The range of column positions will be d! for d positions. This paper then divides RRW texture image, into a 3 X 3 grid and derives cross and diagonal rule based rank words. From this, the present paper derived Rule based Rank Word-Cross and Diagonal Texture Matrix (RRW-CDTM) and derives GLCM features for effective texture classification. The experimental results on various texture databases revels the classification accuracy of the proposed method. The proposed method is compared with the state of art local based approaches.

A Novel Approach in Adopting Finite State Automata for Image Processing Applications

2018 ◽  
Vol 8 (1) ◽  
pp. 59-74
Author(s):  
R. Obulakonda Reddy ◽  
Kashyap D. Dhruve ◽  
R. Nagarjuna Reddy ◽  
M. Radha ◽  
N. Sree Vani
Keyword(s):  
Image Processing ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Texture Classification ◽  
Superior Performance ◽  
Finite State Automata ◽  
Processing Application ◽  
Novel Approach ◽  
Comparison Results ◽  
Finite State

This article describes how robust image processing application rely heavily on image descriptors extracted. Limited work is carried out in adopting probabilistic finite state automata (PFSA) models for image processing. A finite state automata for image processing (FSAFIP) method is presented here. Texture classification and content based image retrieval (CBIR) is considered. In FSAFIP, foreground and background regions of an image are identified and later split into patches. Using a tristate PFSA model, feature descriptors corresponding to background/foreground regions are constructed. A distance based large margin nearest neighbor (LMNN) classifier is considered in FSAFIP to impart intelligence. A performance and experimental study to evaluate performance of FSAFIP for CBIR and texture classification is presented. Comparison results in CBIR obtained prove superior performance of FSAFIP over existing methods on Corel-1K dataset. High texture classification accuracy of 99.2% is reported using FSAFIP on KHT-TIPS dataset. An improved texture classification accuracy is achieved using FSAFIP in comparison to former methods.

scholarly journals NEURAL NETWORK BASED CLASSIFICATION USING BLUR DEGRADATION AND AFFINE DEFORMATION INVARIANT FEATURES

2001 ◽  
Vol 10 (01n02) ◽  
pp. 243-256 ◽  
Author(s):  
YANI ZHANG ◽  
CHANGYUN WEN ◽  
YING ZHANG ◽  
YENG CHAI SOH
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Pattern Analysis ◽  
Moment Invariants ◽  
Normalization Procedure ◽  
Image Normalization ◽  
Invariant Moments ◽  
High Classification Accuracy ◽  
Affine Deformation ◽  
Degraded Images

Identification of affine deformed and simultaneously blur degraded images is an important task in pattern analysis. In this paper, we introduce an image normalization approach to derive blur and affine combined moment invariants (BACIs). In our scheme, the lowest order blur invariant moments are used as the normalization constraints and an appropriate normalization procedure is designed to guarantee that the constraints used in each step should not be affected in the subsequent normalization steps. A neural network (NN) model is then employed to classify the degraded images using the proposed BACIs. Experimental results show that the system has high classification accuracy.

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