A CT lung provides images of the lungs from three directions; top, bottom and middle. Local Binary Pattern (LBP) is one feature extraction technique that can be used in image classification. Derivatives that are not common are Center Symmetric Local Binary Pattern (CS-LBP), Local Binary Pattern Variance (LBPV) and Complete Local Binary Pattern (LBPV).
The main idea of LBP is that the LBP operator will extract the image feature and express it in the form of a histogram. First and foremost I would like to express my gratitude to Allah for His guidance and blessing to complete my senior year. I am grateful to her for her unwavering commitment to this last year's project.
I conclude my acknowledgment to all those who contribute directly and indirectly in my success to complete this last year project. Without them I would not be able to complete my final year project entitled "Texture Classification of lung computed tomography (CT) using local binary patterns (LBP)".
INTRODUCTION
Background
2 The appearance method works with several techniques to determine the severity of the disease. People start following the techniques with various modifications to see the accuracy of the method. Ojala et al showed in their paper that LBP can be calculated in a simple way and can be modified to see the results in a remarkable way.
Due to the variation in the textures or images, the LBP operator may not work properly. In other hands, the change of images or textures can be varied depending on the type of experiment. The brightness, contrast and colors of the textures can be a factor to see the change of LBP's value.
Therefore, the accuracy of LBP can be changed depending on the characteristics of the images or textures. In this experiment, we try not to change the original spatial algorithm of the CT scan features to get the pure results without modifying the features.
Problem Statement
Objectives
LITERATURE REVIEW
Techniques
- LBP – Local Binary Pattern
- CS-LBP – Center Symmetric Local Binary Pattern
- LBPV – Local Binary Pattern Variance
- CLBP – Completed Local Binary Pattern
A uniform pattern of LBP is another extension of the method of mapping the LBP markers. Quantization is necessary for the variation of the images by pixel and radius. Due to the different contrasts of the images, the variability of LBP was increased.
If most of the samples are close to the value of a particular class, the sample will be represented in that specific class. Only unfiltered LBP values and histogram enhancements could affect the kNN classifier. We will see this matter in Chapter 4 of this report. The kNN classifier works based on the Euclidean distance.
The closer the distance to the point, the more accurate the value response will be. The inner circle showing k = 3, where the outer circle k = 5. Different k will give different classification results. The size of the image that is retrieved from the database also plays a big role.
The images are separated based on the type of lung tissue disease. Surprisingly, the analysis of the CLBP has the same outcome as the conventional or normal LBP. Therefore, the outcome or results can be expected to be the same, even the value of both LBP plays a major role.
The accuracy of LBPV is slightly lower compared to CLBP and LBP. Based on the theory of CSLBP, half of the pixel is focused on patches. After that, the kNN classifier will be used to classify and evaluate the reliability of LBP.
Classifier
- kNN classifier – k Nearest Neighbor classifier
METHODOLOGY / PROJECT WORK
Lung Classification
To make it clearer what the LBP is all about, you can refer to the image below. According to Lukas Apalovic in his summary of LBP, there must first be texture and texture classes. In this case, by converting the bitmap image to the grayscale image, each pixel of the image has its own pixel.
Based on Apalović's explanation, there will be an LBP value from each pixel. left) pixel from ROI an. center) pixel representation. From the LBP theory given by Pietikäinen, the value can be calculated using the LBP formula. Since 1.1 pixels is the center, the eigenvalue will be compared to the neighbor's value.
If the value of the neighbor is less than the center, it will show 0. When the value is bigger, it will show 1. Currently, 3 types of maps are made for the project, rotation invariant LBP, uniform LBP and rotation invariant LBP uniform rotation. . For texture classes, there is a classifier to classify the texture based on the class it comes from.
For this project, the matrix shape representation will be used to undergo the classification process using the k-NN classifier. CT scanning was performed using General Electric (GE) equipment (LightSpeed QX/i; GE Medical Systems, Milwaukee, WI, USA) with four detector rows and using the following parameters: in-plane resolution 0.78 x 0.78 mm, slice thickness 1.25 mm, tube voltage 140 kV, and tube current 200 mAs. The middle image is the CT scan of the lung from below (b) and the rightmost is the top view (c).
Cycle of Experiment
RESULT & DISSCUSSION
The feature extraction techniques of LBPs
- LBPs in less database of emphysema
CONCLUSION AND FURTHER RESEARCH WORK
Conclusion
LBP histograms can be extracted from all patches and combined into a matrix. The hypothesis of this project is that the local binary pattern can be used to analyze the structure of the lung. In conclusion, based on the results of the LBP experiment, it still cannot be concluded as the best method for lung texture.
This is because in future research, there are still a few variants that can be used for lung texture analysis technique, and some changes need to be made to the LRP.
Further research work on LBP
34;Global Initiative for Obstructive Lung Disease (GOLD) classification of lung disease and mortality: findings from the Atherosclerosis Risk in Communities (ARIC) study. 34; Illness perception in people with chronic obstructive pulmonary disease.". 34;An introduction to nonparametric kernel and nearest neighbor regression". Zhang, “Rotation invariant texture classification using LBP variance (LBPV) with global matching,” Pattern Recognition , vol.
Zhang, “A completed modeling of local binary pattern operator for texture classification,” Image Processing, IEEE Transactions on, vol.
