Support Vector Machine (SVM)

SVM is a supervised machine learning approach which has been successfully demon- strated for cancer classification and medical diagnosis,especially with the high dimen- sional feature spaces and relatively small sample size.(Chakraborty,2011). In addition, the ability of the SVM to perform both linear and non-linear classification. The main idea of the SVM is find an optimal hyperplane by maximise the margin to separate the data into classes, as illustrated in Figure5.26.

Chapter 5. Liver Lesion Characterisation and Classification

Regarding the Figure5.26, the hyperplanes H1and H2 is defined in Equation5.33

where H0 is the median in between H1 and H2. The margin (m) of a separating hy-

perplane is represented by m = d+_{+ d}− _{where d}+ _{and d}− _{is the shortest distance to}

the closest positive and negative point respectively, respect to H0. The total distance

between H1and H2 is defined as 2/kwk

H1 : w • xi+ b ≥ +1 when yi = +1

H2 : w • xi+ b ≤ −1 when yi = −1

(5.33)

Where x is the input sample features, y is the output class, w is the weight vector and b represents a bias.

The performance evaluation of the proposed method for liver lesion characteri- sation and classification will discuss in the next Chapter 6. The experiments were performed using tenfold cross-validation and train/test spilt validation. However, The SVM classifier with Radial Basis Function kernel (SVM-RBF) was chosen because it provides the best results when compared to other classifiers that were tested. Further- more, it can classify multi-dimensional data, unlike a linear kernel function and it has fewer parameters to set than a polynomial kernel. Two main RBF parameters applied in SVM are C and γ. Parameter C represents the cost of the penalty and parameter γ is the width of the kernel function.

5.8

Conclusion

This chapter presented the proposed thesis framework technical design details for liver lesion classification and characterisation. The framework was built to a better model of lesion classification and characterisation through linking between low-level features, high-level features and ROI. In addition, dealing with the challenge of the region of interest selection method that represent the lesion characteristics. Thus, the difference- of-features and multiple ROIs were developed for robust capturing of lesion charac- teristics in a reliable way. Furthermore, in contrast to the previous techniques that operate mainly over the lesion area with no pay attention to the relation between lesion and liver. The design of the liver lesion characterisation framework was inspired by an understanding of the radiologists’ vision to characterise lesions as well as the utilising of prior knowledge of the medical background to support its robust performance.

This section presented a new technique for liver lesion characterisation, based on high-level features extracted automatically from the CT image, to simulate the clinician observation in describing liver lesions. In addition, the proposed framework presents three different methods to classify liver lesion. Liver lesion classification based on low-level features. Enhancing the lesion classification accuracy through utilising the

high-level features to classify the respective lesions, with the benefit of interpretable characterisation that supports the diagnostic decision. These results will be presented in the next chapter. The combination between low-level features and characterisation were used to build an extended feature vector. In summary, the chapter contributions are:

• Developing an automated technique for liver lesion characterisation in order to predict radiological observation in describing the liver lesions through using low-level features extracted from computed tomography (CT) images to infer higher level features, and simulate radiological observations for liver charac- terisation. In addition, overcoming the challenge of linking the image content through converting the low-level features to visual semantics by lesion charac- terisation. Thus, Assigning high-level descriptions to the liver lesion in analogy to radiologist observation.

• Proposing a novel Multiple ROIs for liver lesion classification/characterisation. The proposed method is based on medical knowledge and classifies the region of interest into three areas (inside lesion, lesion border and surrounding lesion) through constructing a multi-level abnormality map based on the intensity differ- ence with respect to the normal liver. In addition, the asymmetry and compact- ness features are computed to define the probability of each level to represent a lesion. Thus, three regions of interest are defined and known as Multiple image ROIs. The idea behind the multiple image ROIs is to capture all the lesion ap- pearance characteristics by considering the ability of each ROI that represents a set of lesion characteristics. This is in contrast with most existing research, which mainly relies on lesion area without considering the effect of the lesion on the surrounding area, where the performance of classification/ characterisa- tion could be affected due to the selection of ROI methods, which represents the characteristics of the lesion.

• Proposing a difference-of-feature (DoF) technique to enhance the classification/ characterisation performance. The idea of the DoF is identifying the difference between the lesion and the surrounding normal liver tissues. The DoF empha- sises the relative difference of the lesion properties, in relation to surrounding tissues of the same patient, regardless of the demographics or imaging device. Then, the two proposed techniques (DoF and Multiple ROIs) are combined to- wards a better and robust classification/characterisation results.

• Building a new classification framework for classifying liver lesion in three dif- ferent novel ways, as follows:

1. Classifying lesion based on low-level features that are extracted from a Multiple ROIs (inside, border and outside lesion). The multiple ROIs fused

Chapter 5. Liver Lesion Characterisation and Classification

with difference-of-features between the lesion itself and its surrounding area. This is in contrast with most existing research, which focus on ex- tracted features from the segmented lesion only. The Multiple ROIs and difference-of-features emphasise the relative difference of the lesion prop- erties, in relation to the surrounding tissues of the same patient. It also captures the different property/behaviour between benign and malignant lesions and how they affect the adjacent tissues.

2. Utilising the high-level features that characterised the lesion to build a novel feature vector. The new feature vector is used to classify the respec- tive lesions, with the benefit of interpretable characterisation that supports the diagnostic decision in analogy to radiologist observation, which is in contrast with the existing works that used the black box low-level features that cannot provide an explanation in human level understanding for the di- agnostic decision. However, the classification based on high-level features is more reliable for the radiologist, which provides the understanding ex- planation for the diagnostic decision in analogy to radiologist observation. 3. Building a novel feature vector that composed of the combination of low- level features and high-level features. The new feature vector is used to enhance the classification accuracy. In contrast with the existing works that used only low-level features. However, the classification through the fusion between high-level and low-level features will lose the advantages of the characterisation and track back to diagnosis explanation.

The next chapter will further discuss the liver lesion classification/ characterisation per- formance with more evaluation across the dataset. Furthermore, the proposed frame- work will be benchmarked against a number of state-of-art baselines in the next chapter as well.