Feature Extraction and Analysis of MRI Images

Abstract

Breast cancer is a disease that starts in the breast with a malignant tumor. A malignant tumor is a mass of cells that grows out of control. The cancerous cells can also metastasize, or move to other tissues or parts of the body. The cancer can develop in any of the three types of breast tissue: lobules, ducts, and connective tissue. Breast cancer that spreads into normal tissue is called invasive breast cancer. Noninvasive breast cancer stays within the breast lobule or duct. Feature extraction is a process of image processing which is used to select and extract those features/properties which are helpful in identifying the problem of interest. It is a methodology followed not only in digital image processing but also in machine learning, pattern recognition and computer vision. Feature extraction involves reducing the amount of resources required to describe a large set of data. When performing analysis of complex data one of the major problems stems from the number of variables involved. Analysis with a large number of variables generally requires a large amount of memory and computation power, also it may cause a classification algorithm to overfit to training samples and generalize poorly to new samples. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy. Many machine learning practitioners believe that properly optimized feature extraction is the key to effective model construction. This paper focus on some symptoms and causes of breast cancer, some literature survey on Feature extraction.

Keywords:-Cancer, Malignant tumor, feature extraction, machine learning