A Review of Existing Texture Analysis Methods for US Images

In the medical domain, interesting results of using texture analysis methods were reported. In the area of ultrasound images, many studies have been carried out to characterise the B-Mode images. Texture analysis of ultrasound images is motivated by the principle that if disease processes affect the structure/texture of tissues, then the diseased tissue should reflect ultrasound wave signals in a different way than that of normal tissue (Morris 1988), i.e. texture features value extracted from the US scan of diseased tissue differs from those extracted from US scanned healthy/normal tissue. The transformation of cancerous tissue, for example, will result in the changes in the tissue characteristics. Therefore, it is expected that textural features derived from cancerous tissue and normal tissue will differ.

Texture analysis has been used in ultrasound image analysis for different applications. In (Malathi and Shanthi 2010), a method for automatic classification between ultrasound images of normal and abnormal placenta was reported. The textural feature was extracted from the ultrasound image of placentas based on statistical properties of intensity histogram such as mean, standard deviation, contrast, correlation, and entropy. Another study showed that grey level co-occurrence matrices (GLCM) features derived from ultrasound images have a significant difference between the placenta of smoker and non-smokers where fourteen features were extracted from the GLCM matrices and in four different angles 0°, 45°, 90° and

92 135° (Morris 1988). The extracted features were an angular second moment, contrast, correlation, inverse different moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, two information measures of correlation, autocorrelation, and absolute value.

Furthermore, a similar method based on the fourteen GLCM features in the four dimensions was proposed in the area of automatic detection of heart disease (Tsai and Kojima 2005), The features were extracted from ultrasound images to classify heart disease where four features out of the fourteen were identified to have the most powerful discrimination ability (angular Second moment, contrast, correlation and sum entropy).

The texture analysis has also been applied extensively on ultrasound images for liver related diseases. Statistical-based texture analysis (GLCM) matrix was utilised to evaluate the texture features of the liver tumours in (Xian 2010). Five textural features were extracted from the GLCM matrix (energy, contrast, correlation, entropy and homogeneity) using the average of the four directions. It was demonstrated that the method can distinguish between benign and malignant tumours of the liver. Moreover, in (Mojsilovic, Popović et al. 1998), wavelet transform features were used to capture texture characterisation of B-mode liver images where the energies of the transformed regions were used to characterise the textures.

Texture analysis of ultrasound images has been also applied on thyroid gland. A promising result was reported in (Smutek, Šára et al. 2003) in which chronic inflamed thyroid tissues were automatically differentiated from healthy ones. The extracted features were based on the following six spatial features: gradient magnitude, difference from sample mean, horizontal curvature, vertical curvature, and original pixel grey levels. The six features were then combined with the following nine haralick texture features: cluster tendency, texture entropy, texture contrast, texture correlation, texture homogeneity, inverse difference moment, maximum probability, and uniformity of energy.

Furthermore, texture analysis was used to discriminate between benign and malignant breast tumours from ultrasound images. In (Lefebvre, Meunier et al. 2000), the texture parameters, derived from first-order statistics, run-length matrices and co-occurrence matrices. It was argued that texture features were able to help physicians in reducing the number of unnecessary biopsies. In (Garra, Krasner et al. 1993), four different types of ultrasound images of breast tumours were studied namely, cancers, cysts, fibroadenoma, and fibrocystic tumours. The analysis of image texture was performed using fractal analysis and

93 statistical texture analysis methods. The most useful features were those derived from the GLCM matrices based on the contrast and correlation features. It was argued again that ultrasonic image texture analysis is a simple way to significantly reduce the number of benign lesion biopsies without missing additional cancers.

Besides breast cancer, texture analysis has been also applied on ultrasound images of prostate. In (Sheppard and Shih 2005) GLCM is generated based on the above fourteen extracted features from prostate ultrasound images. Then, five of the fourteen features were chosen namely, angular second moment, contrast, inverse difference moment, entropy, and sum entropy. These features were successful in distinguishing between normal and cancer tissues in prostate US images.

A rather limited research has been conducted to evaluate texture analysis on US images of different types of ovarian tumours. In (Sohail, Rahman et al. 2010), an automatic method was proposed to classify three different types of benign ovarian tumours namely; Simple Cyst (187-images), Endometrioma (154-images) and Teratoma (137-images) in total 478 images were used. The features were extracted from statistical texture using 64 features based histogram moments along with 56 features extracted from GLCM in four directions. An average of classification accuracy 86.90% was achieved to identify different types of benign tumours.

In (Acharya, Vinitha Sree et al. 2012), Higher Order Spectra (HOS) features were used for the characterisation of different types of US images of ovarian tumours namely, Benign and Malignant. A small dataset of 20 patients (10 benign and 10 malignant) was used to evaluate the method where each patient has 100 images i.e. 1000 benign and 1000 malignant ultrasound image in total. A classification accuracy of 95.1%, sensitivity of 92.5% and specificity of 97.7%, was achieved to identify different types of ovarian tumours.

More research on automatic identification of US images of ovarian tumour was reported in (Acharya, Mookiah et al. 2013) where three different types of features extracted from each ultrasound image of 20 patients (10 benign and 10 malignant). These features are: Hu’s invariant moments features (invariant to object scale, position, and orientation), 2D Gabor wavelet features at six directions, and Yager’s measure and Kapur’s entropy to estimate the subtle variation in the pixel intensities. An average classification accuracy of 99.8 %, sensitivity of 99.2 % and specificity of 99.6 % was reported to identify different types of benign tumours.

94 To the best of our knowledge, there is no publication in the literature on using texture analysis in the area of gestational sac to identify early miscarriage cases.