Textural Analysis

Besides quantification of MRI biomarkers such as CRM, EMVI and TRG, there has been a greater recent interest in quantification of imaging biomarkers linked to underlying intra- tumour heterogeneity associated with adverse outcomes in terms of treatment failure and drug resistance (Ganeshan, et al., 2013). Heterogeneity can be quantified on imaging non- invasively using textural-analysis (TA). The interpretation of radiological images is based on the naked eye examination. Yet there are features in the images that can yield a greater degree of information by analysing its textural properties. TA assesses the distribution of pixel grey-level intensity, coarseness and regularity in digital images (Castellano, et al., 2004).

There are four steps involved in analysis of medical images for computer aided diagnosis described by Sharma, et al. (2008). 1) Image filtering, 2) segmentation, 3) feature extraction, and 4) analysis of extracted features. The main aim of image filtration is to suppress unwanted photon noise and to enhance image features important from further analysis point of view. Application of band pass filters in statistical textural analysis is used to highlight different spatial scales. One such filter is Laplacian of Gaussian band pass filter. Different filter values or width will enhance specific structures corresponding to that filter value, while structures less that this filter value will become blurred (Ganeshan, et al., 2009). Lower filter values (filter 0.5-1.0) will highlight structures with fine textures, and higher filter values highlight structures with medium (filter 1.5-2.0) and coarse (filter 2.5) textures in the filtered image (Davnall, et al., 2012).

Segmentation is the process that divides the image into various regions of similar properties based on their texture, grey-level, colour or contrast. Digital images used in clinical practice are usually stored in the computer as a two dimensional array and is made up of mutually related small picture elements called pixels. Each pixel has a value that represents the grey- level intensity of that picture. According to Haralick (1979), pixels grey-level intensities and

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their spatial relationship gives image a fine, smooth, coarse or grainy texture. Tuceryan and Jain (1998) defined image texture as a function of the spatial variation in grey-level pixel intensities. Texture analysis, thus is the evaluation of the position and intensity of pixels, in digital images (Castellano, et al., 2004). Texture features produced as a result of the analysis are in fact the complex mathematical parameters computed from the distribution of pixels. These features thus represent the underlying texture type.

Feature extractions and analysis relates to patterns recognition and their quantifications. There are different techniques for textural analysis that can be categorized into four main types: structural, model based, statistical and transform methods (Tuceryan and Jain, 1998). The description of each of these techniques is beyond the scope of my thesis. More commonly used methods in analysis of medical imaging are statistical and transform methods and will be mainly discussed here. Statistical methodology is the most widely used and it measures the distribution and relationships of grey-level values in the image. Texture parameters derived from these methods are ranked into first, second and higher order parameters. First and second order parameters are more commonly used in medical image. First order statistical parameters include histogram of an image and its variance and are dependent on the individual grey-level value of a pixel without taking into the consideration of spatial interaction between the pixel values. Parameters derived on the basis of histogram analysis, include mean, standard deviation, uniformity (in-homogeneity) entropy (irregularity of intensity distribution), skewness (asymmetry of the histogram), and kurtosis (flatness of the histogram) (Davnall, et al., 2012). First order statistics are not suitable if image has got more than one texture or non-random spatial distribution of pixels (Prats-Montalban, De Juan and Ferrer, 2011). Second order statistical parameters analyse the grey-level distribution of pixel pairs in the image at a random location and orientation relative to each other. Gray level co-occurrence matrix (GLCM) proposed by Haralick (1979) is the most widely used texture feature. A GLCM matrix contains a number of rows and a number of columns equal to number of gray level intensities that shows the frequency of a pixel location

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relative to each other pixel for a given distance and angle (Prats-Montalban, De Juan and Ferrer, 2011). Haralick (1979) described up to 14 textural features that can be computed from GLCM, however more commonly used are variance, contrast (difference between the highest and smallest values of pair of pixels considered), Entropy (disorderliness of the matrix), dissimilarity (heterogeneity of the grey levels), homogeneity (uniformity of the matrix), Correlation(e linear relationship between the grey levels of pixel pairs) and energy( consistency/orderliness of textural information) (Mridula, Kumar and Patra 2011). Another second order statistics is Run length matrix (RLM) that measures the runs of pixels with same grey level intensity in a particular direction. The average of these run lengths is a measure of coarseness of a texture. More small runs with similar grey level intensities will form a smooth texture as compared to long runs with different grey level intensities that would form a coarse texture.

Higher order statistics estimate properties of three or more than three pixel values occurring at specific locations relative to each other. Amadasun and king (1989) categorized them into coarseness (measures edge density) contrast (measures intensity difference between neighbouring regions), busyness (measures spatial frequency of intensity changes) and complexity (measures sharp edges and lines). In Transform based analysis, textural features are defined by spatial frequencies. Fine textures are rich in high frequencies, whereas coarse textures are rich in low frequencies. They include Fourier, and Wavelet transforms. Fourier transform gives a global sense of the frequency characteristics of an image but lacks spatial localization and hence not very popular. This problem can be overcome by using wavelet filters that allows the analysis of frequency content of an image at various resolution scales with minimal loss of information (Prats-Montalban, De Juan and Ferrer, 2011). Another popular model-based method in medical imaging process is based on the concept of fractal geometry. This concept is based on the natural objects having statistical self-similar fractal sets at different scales. In other words the variations in the object have the same distribution as the whole over a range of different scales. Fractional dimension is a measure

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of variation at different scales and determines the roughness of surface. For an image, the fractal dimension is related to the variation in image intensity at different scales or pixel ranges (Tuceryan and Jain, 1998). In general features based on statistical methods have more discriminatory powers in image quantification than transform methods (Conners and Harlow, 1980).

2.1.4.1 Application of textural analysis in medical imaging

Textural analysis of medical imaging is not a new idea. In early 1970, it was first applied to plain radiographs (Harlow and Eisenbeis, 1973). Chen, Chang and Huang (1999) used it to characterize solid breast nodules on ultrasonography. In the last decade, there has been renewed interest in textual analysis of medical imaging due to advances in computer technology and development of new textural analysis algorithms and increasingly applied to CT, MRI and PET imaging. In oncological imaging based studies, textural analysis has emerged as diagnostic, prognostic and treatment response assessment tool.

2.1.4.2 Tumour Heterogeneity

Heterogeneity is a well-known feature of tumours and is associated with adverse outcomes in terms of treatment failure and drug resistance (Mroz and Rocco, 2013). Intratumour heterogeneity can be related to both genetic and histopathological variations such as cellularity, angiogenesis, extravascular extracellular matrix, and areas of necrosis (Davnall, et al., 2012). Gerlinger, et al., (2012) in their study showed that intratumour genetic heterogeneity varies both in space and over time. They also demonstrated that single or random biopsy of tumour may not represent the full extent of intratumour heterogeneity due to sampling error. Therefore it is important to identify the imaging biomarkers that can be correlated with worse histopathological features such as tumour grade, hypoxia and angiogenesis. Heterogeneity can be quantified on imaging using textural analysis which provides the non-invasive method of assessment. The study by Ganeshan, et al., (2013) identified the biological correlates for tumour hypoxia and angiogenesis on the basis of

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textual analysis of CT images. This study showed significant association between medium to coarse texture scale and angiogenesis and hypoxia makers in primary non-small cell lung cancers.

2.1.4.3 Lesion detection and characterization

Texture analysis helps in characterizing lesion into benign or malignant based on their texture differences. In the study by Kido, et al., (2002), fractal analysis of gray-scale images of <2cm small periphery nodules, showed that fractal dimensions for organizing pneumonias and tuberculomas were greater than those of bronchogenic carcinomas ( p < 0.0001) and hamartoma ( p < 0.0001). Similarly the study by Gibbs and Turnbull (2003) showed significant differences for second order co-occurrence matrices such as contrast, variance and sum entropy between benign and malignant breast lesions when applied to high resolution contrast enhanced MRI images. These findings supported the general perception that benign lesions are homogenous compared to malignant lesions. They also showed, combining textural analysis with other parameters such as lesion size, age and time to maximum enhancement, can achieve diagnostic accuracy of 0.92 ± 0.05. Other studies showed the potential of textural analysis in differentiating malignant and benign lymph nodes in rectal cancer (Cui, et al., 2011) and differentiating colon cancer and normal colonic mucosa (Goh, et al., 2009).

2.1.4.4 Treatment response

Imaging biomarkers based on Textural analysis also helps in improving the predictive response to a cancer treatment. In a study of 39 patients with metastatic renal cell cancer receiving tyrosine kinase inhibitors (TKI), analysis of changes in CT textural parameters after 2 cycles of TKI were better predictor of response than conventional response evaluation criteria in solid tumours(Goh, et al., 2011). Percentage change in coarse texture uniformity of ≤ 2 % was an independent factor that correlated with shorter time to progression. O‘ Connor et al. (2011) studied 10 patients with 26 liver metastasis and

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showed that tumour heterogeneity measured by fractal dimension on pre-treatment MRI predicted shrinkage in tumour volume after 5 cycles of anti-angiogenic and cytotoxic chemotherapy. In a recent study of 100 breast cancer patients who received chemotherapy , textual analysis (GLCM-Matrices) of dynamic contrast enhanced-MR images showed significant differences for the contrast (p value=0.042) and difference in variance(p value=0.043) parameters between responders and non-responders (response determined by greater than or less than 50% change in largest diameter)(Ahmed, et al., 2013). Higher values of contrast (a measure of local image variation) and difference in variance (a measure of variation in the difference in gray levels between pixel pairs) found in the study supported the fact that heterogeneous tumours will respond poorly to the chemotherapy. These differences were more significant at 1-2 minute post contrast image time and no significant differences were observed in pre contrast images. Textural analysis of fluorodeoxygenase (FDG) uptake heterogeneity on pre CRT 18F-FDG PET images of patients with oesophageal carcinoma was assessed by Tixier, et al., (2011). Co-occurrence matrices strongly differentiated non responders from partial responders.

2.1.4.5 Prognostic tool

To date few studies have explored the potential of textural analysis as a prognostic tool for cancer survival. These studies are confined to CT or PET-CT based textural analysis. Ganeshan, et al. (2012) carried out two separate pilot studies on non-small cell lung cancer (NSCLC) (54 patients) and oesophageal cancers (21 patients). The studies showed that histogram based textural analysis of pixel distribution (first order statistics) of unenhanced CT images obtained using PET-CT examinations was significant independent predictor of poor survival for both NSCLC (P=0.001) and oesophageal cancer (P=0.0006). In a separate study by the same group (Ng, et al., 2012); textural analysis of primary colorectal cancers (57 patients) was done to determine its relation with overall survival. Textural analysis showed that at a filter value of 1.0, entropy, uniformity, kurtosis, skewness, and standard deviation of pixel distribution were separate independent predictors of poorer 5-year overall

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survival. These results differ from the other studies as it showed that primary tumours with less heterogeneity at fine-texture level showed association with worse prognosis. But this study assessed whole-tumour volume rather than a single axial level and contrast enhanced CT images were analysed unlike previous studies. The author postulates that this may also be due increased vascular permeability of tumour cells that itself has shown to be associated with advance tumour stage and worse survival in colorectal cancer patients (Yonenaga, et al., 2005). Increased vascular permeability leads to greater cell packing resulting in uniform distribution of vascularisation and greater parenchymal enhancement. In another study, coarse uniformity texture of liver in patients with non-metastatic colorectal cancer was shown to be effective marker of survival than hepatic perfusion CT (Miles, et al., 2009).