## 2 Research Status and Development Trends for Computer Aided Detection and Diagnosis of MRI
### Image Feature Extraction of Breast Tumours and Pattern Classification of Breast Fibrous Glands  
Women's breasts are composed of lobules, ducts and fatty tissues. Most often, epithelial tumours start their growth inside the lobules and ducts; as tumours spread to other tissues of the breast [7], they eventually metastas to other parts of the body. Although breast tumours are often referred to as one disease, there are many different types of breast tumours. Tumour cells can vary in location (milk ducts or lobules) and morphology when observed under a microscope. Additionally, morphological differences contain very useful information that enables an accurate prognosis. Breast tumours can be divided into the following two main overarching categories: (i) Benign tumours that are considered as noncancerous and non-life-threatening and (ii) Malignant tumoursthat are characterised by abnormal cell growth and might rapidly spread or invade nearby tissues. Morphologically, the nuclei of the malignant cell may be easily identified as they are much bigger than in normal cell. Since benign tumours can turn malignant, monitoring is advisable. A well established image assessment technique adopted by radiologists to assess disease proliferation is the Breast Imaging-Reporting and Data System (BI-RADS), which is published by the American College of Radiology (ACR) [1]. This methodology sub-divides clinical observations into 6 categories which are normally assigned by the radiologist. Different perspectives of breast images have been investigated according to the prevalence of the disease, its degree of advancement and the quality of the images. Classification on the basis of the above parameters is then performed using machine learning (ML) and Artificial Intelligence (AI) approaches. A robust feature detector actually provides information on whether a particular feature is present in an image or not. There are four groups of features: empirical, structural, statistical and deep learning features, as illustrated in Figure 1. Empirical features are mainly proposed by radiologists and they qualitatively describe features verifiable by observation or experience rather than through a mathematical expression. Structural features provide information about the observed features characteristics such as structure, orientation and area, defining a convex hull describing a shape or region and centroid; this way a more mathematical description can be attributed to the observed features. Statistical information regarding the observed features in tumour images include the area of the nucleus or the centroid of the mass. Mean, Median and Standard Deviation can provide some potentially important information on the observed features and also have some diagnostic potential. Thus statistical features can be also used as additional information at the input of the classifier. An alternative approach to improve the classifier performance is to invoke deep learning strategies. In deep learning, features are extracted from data directly, using non-linear inference engines in a similar way to that of an expert performing the diagnosis. The advantage of such approach is that the output of a classifier can be related to clinical outcomes more naturally. Generally, deep learning features are extracted from convolutional layers in the deep learning model. Each convolutional layer can include hundreds of convolutional filters, which can be defined as deep learning features. The deep learning features can describe multi-level tumour information from low-level (visual characteristics) to high-level (more abstract features) [8]. In a conventional image classification task, features are extracted locally using specific rules. Local texture and statistical features are universally accepted as the more important features for  
Figure 1: Radiomic analysis simultaneously explores empirical features, statistical features, structure features and deep learning. breast image classification.