Liver Lesion Characterisation and Classification
5.3.3 High-level Features Based on Machine Learning
In this section, we present the proposed liver lesion characterisation framework, based on machine learning, that emulate the human understanding of liver lesion images. Fig- ure5.17 displays the proposed framework overview to characterised the lesion based on machine learning. The proposed system needs to be trained through using low-level features that extracted from the lesion as an input and the high-level description as an output.
Figure 5.17: Overview of the lesion characterisation that is inferred from the low-level features through machine learning process.
Table 5.11 shows the generated high-level features that used to characterised the lesion based on machine learning. The learning process is utilised to linkage between
Chapter 5. Liver Lesion Characterisation and Classification
low-level features and high-level features through selecting the best-related region of interest (Multiple ROIs) to infer the high-level features by considering the ability of each ROI that represents a set of lesion characteristics.
High-level feature Characterisation
Lesion density Hypodense, Hyperdense, Isodense, NA*. Lesion density type Heterogeneous, Homogeneous, NA. Lesion rim
Continuous Bright Rim, Discontinuous Bright Rim, Continuous Dark Rim, Discontinuous Dark Rim,
NA. Lesion rim thickness Thick, Thin, NA.
Contrast Uptaken Heterogeneous, Homogeneous, Dense, NA. Enhancement Pattern Hypoattenuation, Hyperattenuation, Isoattenuation,
NA.
Lesion composition Solid, Cystic, Mix, NA. Lesion leveling type fluid fluid, fluid gas, fluid solid, NA. Lesion shape Irregular, Ovoid, Round, NA.
Lesion margin Smooth, ill defined, well defined, Irregular, NA. Lesion margin defini-
tion Defined, Diffuse, NA.
Lesion enhancement Enhancing, Hypervascular, Nonenhancing, NA. Lesion brightness Hyperdense, Hypodense, Water density, NA. Lesion surrounding Complete, Incomplete, Absent, NA.
* NA is not applicable.
Table 5.11: The high-level features inferred from the low-level features based on ma- chine learning to characterised liver lesion.
The lesion characterisation was reached based on segmenting the lesion and sur- rounding liver tissue with image processing methods. According to the enhancing lesion rim definition, that is an enhancing ring around the lesion (peripheral enhance- ment), the rim may be thin (< 1cm) or thick (> 1cm) (Elsayes et al., 2005; Martin et al.,2010;Jang et al.,2013). The area that surrounds the lesion from the liver tissue with size (1.5cm) is added to the segmented lesion and defined as (AROI), To ensure
the capture of all lesion rim characteristics (thick / thin). The segmented area (AROI)
was divided into three areas which defined as Multiple ROIs. Namely, The inner lesion (LRin) and it considers that the pixels located between the central of the lesion and le-
sion border. The second area is lesion margin (LRm) and denotes as the area between
inner lesion and lesion edge. The area that surrounds the lesion from the liver tissue is taken as the third region (LRout) to capture all the characteristics of the relation
between lesion and liver.
Regarding multiple ROIs selection, the distance map is calculated for the (AROI)
based on the intensity difference and the proximity distance for each voxel with respect to the normal liver tissue to generate the abnormality level map. The fast-marching method (Sethian,1999) is adopted to generate the initial labeled regions. The speed of
fast-marching approach was empirically defined and it is equal 1 in pixels having inten- sity less or same the normal tissue, and 0.2 when the intensity difference is significant, due to the fast-marching approach might get stuck with the high intensity pixels such as calcified area. The abnormality map contains the zero value that represent the liver tissue and denoted by (LRout), and positive values which define the lesion including
the border and denoted by (LR). The further analysis of the abnormal area (LR), start- ing from the lesion centre and iteratively checking the abnormality neighbours in order to reduce abnormality pixels. The asymmetry and compactness features are calculated at each abnormality level to determine the inner lesion area (LRin) with abnormality
level is equal or above the lesion threshold, otherwise defined as lesion margin (LRm).
Formally, consider a set of non-zero area LR = {l1, l2, l3...ln} where LR is the set
of (non-zero) areas in the partitioned abnormality map and n is the number of the areas in the abnormality map. For any area li ∈ LR let m(li) represent the abnormality
value of the area li. For any subset of areas V ⊆ LR let m(V ) = minv∈Vm(v)
indicates the lower abnormality value of areas included in V . let X = LR/V where LR/V = {x ∈ LR |x /∈ V }, for any V ⊆ LR and any li ∈ X, the relation f (V, li)
considered true just only when li is at least neighbour for one area in V . For any
V ⊆ LR let f (V ) = {d ∈ X|f (V, d)} refers to a set of areas that are neighbour to any area in V and let f−1(V ) = {d ∈ f (V )|m(d) = m(V ) − 1} refers to the subset of
the neighbourhood that contains only areas with an equal to the abnormality value of area V subtracted by 1. The area li ∈ LR is considered as a maximum area when just
m(d) < m(li) for all d ∈ f ({li}), as depicted in Figure5.18for the abnormality level
map illustration. The abnormality level b of the area embracing the maximum area li
from the inner lesion is defined in Equation 5.23 to generate the lesion border mask and assigned as lesion margin (LRm).
Vib = ( {li} , b = 0 Vib−1∪ f−1 Vib−1 , 1 ≤ b ≤ m (li) − 1 (5.23)
In order to build the distance map for each abnormality level Vb
i |0 ≤ b ≤ m (li) − 1,
the asymmetry and compactness features are computed for the area (li). These features
are utilised to assign li (abnormality level) of Vil to represent a lesion area where the
Chapter 5. Liver Lesion Characterisation and Classification
Figure 5.18: The abnormality level map for liver lesion; (a) Liver lesion CT image; (b) Liver lesion abnormality map involving in V embracing by liareas.
Regarding our proposed Multiple ROIs, the main idea for generating the level of abnormality map for lesion is to separate the lesion margin (LRm) from the inner
lesion (LRin) area. Figure5.18 depicts a small lesion (5.18.a) and the corresponding
abnormality map (5.18.b). The brightest pixels of the abnormality map represent the maximum area li, where m (li) = 6, as shown in Figure5.18.b. The first abnormality
level surrounding liis Vi1where the set of V contains li and all areas which neighbour
li with a total number of abnormality level equal to 5. Continuing the iteration (b =
2, ..., 5), each level of the abnormality area Vi is assigned as an inner lesion LRin up
to 80% of the lesion area. The remain abnormal level area is assigned as lesion margin LRm. LRin is the area internal the lesion and it considers that more than 80% of
the pixels located in the central of the lesion. Otherwise, the remaining area between internal lesion and lesion boundary is considered as a lesion margin (LRm).
Figure5.19 depicts a summary of the proposed work-flow for liver lesion charac- terisation based on learning process. A set of expert-characterised CT images of liver lesion are utilised to train and validate the proposed framework. The validation meth- ods and attempted experiments will introduce in next Chapter6. The feature extraction block calculates a wide variety of intensity, texture and shape features by considering the lesion characteristics.
Figure 5.19: Proposed framework for liver lesion characterisation based on machine learning process.
A set of quantitative of low-level features were extracted from the segmented lesion (LR) and also from each area in multiple of ROIs that includes the inner lesion (LRin),
lesion margin (LRm) and the rim area of the lesion (LRout). Table 5.12presents the
Chapter 5. Li v er Lesion Characterisation and Classification
Category Low-level Features Represents Dimension
Intensity Histogram Histogram of lesion intensity value. 32
mean Estimation of the average level of intensity value. 1
Standard Deviation Calculates dispersion of intensity value. 1
Skewness Measure of histogram symmetry. 1
Kurtosis Measure of the tail of the histogram 1
Variance The variation of intensity around the mean. 1
Entropy Measure of histogram uniformity. 1
Energy Measure of histogram homogeneity. 1
Texture Gabor energy A 27D vector of lesions Gabor energies in 3 scales and 9 directions. 27 GLCM (Contrast) is a local grey level variation in the GLCM (linear dependency of grey levels of neighbouring pixels). 1 GLCM (Energy) quantifies the repetition of gray level pairs in an image. 1 GLCM (Correlation) assesses the linearity of relationship between various gray level pixel pairs. 1 GLCM (Homogeneity) measures the uniformity of the non-zero entries in the GLCM. 1
Shape 1 Fourier descriptors A 20D vector of the area Fourier descriptors. 20
Smoothness Smoothness of the lesion. 1
Compactness Compactness of the lesion. 1
Sphericity Sphericity of the lesion. 1
Solidity Solidity of the lesion. 1
Roughness Measure of boundary irregularity. 1
Shape 2 Dispersion estimation the irregularity of the lesion. 1
Elongation differentiates the regular oval mass from the irregular. 1
Circularity 1 differentiate circular/ oval lesion from irregular. 1
Circularity 2 differentiate ellipse lesion from irregular. 1
Roundness differentiate circular lesion from irregular. 1
Table 5.12: Low-level features that used for lesion characterisation task.
From an input image, the lesion was segmented as a first step and denoted by (LR). A set of features (Shape 2) were extracted to generate a feature vector that used to char- acterised lesion shape. The (LR) was divided into two areas: inner lesion and lesion margin and denoted by (LRin) and (LRm) respectively. In addition, the surrounding
lesion area from the liver selected as a third region and denoted by (LRout). These three
region defined as Multiple ROIs by considering the ability of each ROI that represents a set of lesion characteristics. The high-level features of lesion margin and lesion mar- gin definition are characterised by extracting the set of features (Shape 1) from (LRm).
All the high-level features (Lesion density, density type, enhancement pattern, lesion composition, lesion leveling type and lesion brightness) are characterised by extracting the intensity and texture feature from both region (LRin) and (LRm) to generate two
feature vectors. The features (LRin) and (LRm) are fused (F VLRin∪ F VLRm) to repre-
sent the mentioned high-level features. The intensity and texture feature are extracted from (LRout) to characterised lesion rim, rim thickness and lesion surrounding. The
difference-of-features of intensity and texture features that extracted from (LRin) and
(LRout) are utilised to characterised the high-level features contrast uptaken and lesion
enhancement.
Table5.13depicts the mapping between high-level, low-level features and selected ROIs that were used to generate the respective high-level features to characterise liver lesions.
High-level feature Low-level features ROI Feature vector Lesion density Intensity + Texture LRin, LRm F VLRin∪ F VLRm
Lesion density type Intensity + Texture LRin, LRm F VLRin∪ F VLRm
Lesion rim Intensity + Texture LRout F VLRout
Lesion rim thickness Intensity + Texture LRout F VLRout
Contrast Uptaken Intensity + Texture LRin, LRm F VDoF(LRin,LRout)
Enhancement Pattern Intensity + Texture LRin, LRm F VLRin∪ F VLRm
Lesion composition Intensity + Texture LRin, LRm F VLRin∪ F VLRm
Lesion leveling type Intensity + Texture LRin, LRm F VLRin∪ F VLRm
Lesion shape Shape 2 LR F VLR
Lesion margin Shape 1 LRm F VLRm
Lesion margin definition Shape 1 LRm F VLRm
Lesion enhancement Intensity + Texture LRin, LRout F VDoF(LRin,LRout)
Lesion brightness Intensity + Texture LRin, LRm F VLRin∪ F VLRm
Lesion surrounding Intensity + Texture LRout F VLRout Table 5.13: The high-level features inferred from the low-level features based on ma- chine learning to characterise liver lesions.
Chapter 5. Liver Lesion Characterisation and Classification