Study On The Method Of Diaginosis Breast Cancer Based On Image

Mammography is the first choice to diagnose mammary diseases, especially the breast cancer. It is of great importance in medical diagnosis and research to study mammogram. The quality of mammogram is poor for the nature of breast tissue. A computer-aided image analysis technique can provide a consistent and reproducible second opinion to a radiologist, which may reduce false-negative diagnosis.Using with digital image processing and techniques of data mining, this dissertation researches and practises some correlation techniques which have got some progresses based on the latest achievements of medical image enhancement, region segmentation, features extraction and optimization, pattern recognition and rule extraction in numerous computer subjects. According to the characteristics of the medical image, algorithm based on the RS theory is improved and compared with the histogram equalization. A region growing method and segmentation algorithm based on deformable model to extract accurate boundary of the breast tumor region are investigated and implemented. Regarding to the large scale of calculation using Apriori Algorithm, an enhanced associative classifier (EAC) is presented, which uses the rough sets theory and association rule.