local binary patterns
Texture feature coding method and local binary patterns are widely used methods in texture recognition and classification as we have previously discussed in Section 2.1. We have applied texture feature coding method and local binary patterns in our study to investigate how these texture features perform in terms of correlating with bone parameters. Although these methods have not been used in bone images to the best of our knowledge, the descriptors from both methods have been used in ultrasound images in several studies described in the next section. This will be a great opportunity for us to use these descriptors and examine their relationship with bone parameters.
2.5.1
Texture feature coding method
In 2001, Horng et al. [39] introduced a new texture analysis method called texture feature coding method for classification of medical images. They also used four other conventional texture analysis methods: GLCM, texture spectrum, statistical feature matrix, and fractal dimension. They compared texture features gen- erated using these methods. They used 30 cases of normal liver, hepatitis and cirrhosis, identified by liver biopsy. They trained a maximum likelihood (ML) classifier for each texture analysis method using those 30 ultrasound images and then tested them on 90 other samples. Their experimental results showed that TFCM outperformed all the other methods with a correct classification rate as high as 86.7. They suggested that although TFCM performed better than other texture features as an independent texture feature, the integration of texture features obtained by different methods may result in even better classification.
Horng et al. in 2008 [50] proposed a study to apply the texture analysis method to classify the dif- ferent disease groups that are normal, tendon inflammation, calcific tendonitis and rotator cuff tear. The supraspinatus tendon is usually involved among above-mentioned diseases progression. Four texture analysis methods that texture feature coding method, gray-level co-occurrence matrix, fractal dimension and texture spectrum are used to extract features of tissue characteristic of supraspinatus tendon. The mutual informa- tion method is independently used to select powerful feature among four texture analysis method, further,
the radial basis function network to classify the ones into the four disease group. Experimental results tested on 85 images reveal that the proposed system can achieve a 84% accuracy rate.
2.5.2
Local binary patterns
Dimitris et al. [42] developed a texture descriptor called fuzzy local binary pattern (FLBP). They incorpo- rated fuzzy logic in LBP methodology. Based on fuzzy rules, LBP pixel values were transformed to multiple fuzzy variables and a FLBP histogram was generated. Ultrasound images were used to evaluate the FLBP performance. Their study showed a 86% accuracy for classifying nodular vs normal thyroid tissues. This study demonstrated that FLBP performed better than other features like GLCM. This study showed that the use of texture analysis based on LBP is promising for medical image texture analysis.
Local binary patterns (LBP) have been used in face recognition. Dynamic texture is also an area where LBP is used. Zhao et al. [51] in 2007 examined two DT databases (MIT and DynTex databases). An extension of local binary pattern, volume local binary pattern (VLBP), was used. Texture was modelled with VLBP by combining motion and appearance. They implemented block-based method to deal with facial expressions in which local information and spatial locations were considered. They showed that classification rates were 100 percent and 95.7 percent using LBP and 100 percent and 97.1 percent using VLBP for the MIT and DynTex databases.
2.6
Discussion
Extensive studies of trabecular bone texture analysis have given an improved way to understand the bone architecture. Various texture methods demonstrated that these methods have great potentiality for bone micro architecture assessment. It has been shown that texture features from 2D radiographs can correlate with 3D bone micro architecture and able to predict osteoporotic fracture. We can find some of the limitations in these studies: soft tissue can greatly affect the bone texture on radiographs and the number of subjects used in these studies are low. However, trabecular properties and fractal analysis of images are important for understanding bone architecture. Texture feature coding method and local binary patterns may improve the assessment of bone micro-architecture. Based upon these findings, we aim to determine the correlation between texture features of 2D images and 3D bone properties. Texture features were extracted from DEI images using GLCM, TFCM, LBP and fractal dimension methods. 3D trabecular bone properties were obtained using micro-CT.
Chapter 3
Materials and Methods
3.1
Image Dataset
Images of human distal wrist bones (radii) were taken from 15 bones. These 15 bones were collected from the University of Saskatchewan anatomical teaching collection. Imaging was conducted at the Canadian Light Source synchrotron on the Biomedical Imaging and Therapy (BMIT) beam lines. The bending magnet beam line was used at 40 KeV with protocols [23]. Images at seven different points (top, 0.5, 0.25, 0.125, -0.5, -0.25, and -0.125) on the rocking curve for horizontal and vertical orientations of each bone were captured. These images are 4008 × 2671-pixel 16-bit float grayscale images with a pixel size of 20µm. Figure 3.1 shows images for horizontal and vertical orientations. These images were preprocessed. Image pre-processing steps are explained with results in detail below.
Figure 3.1: Images of horizontal and vertical orientations