New Approaches to the Design of Automatic Diagnostic Systems and Math Algorithms of Breast Cancer

The present study aimed to investigate the design of a computer-assisted pathology system for diagnosis and clustering of cancerous lesions in magnetic resonance imaging of breast, using computer code in MATLAB software. In the analysis of breast segmentation by Atlas method, mass tumors ۴ and non-mass tumors ۵ are identified and segmented. Characteristics of the morphology, kinetics and matrix of the gray level co- occurrence of the tumors are extracted. In this study, a new feature called “dual-tree complex wavelet transform (DTCWT" was extracted and five characteristics associated with this type of property were extracted. After extracting these properties, the feature vectors were assigned to the clustering with different kernels and the combined clustering, which combine the linear discriminant analysis method and the nearest neighbor, and clustering of the tumors was performed into two benign and malignant categories. Using the new feature introduced in this study and applying it to the SVM cluster, AZ values for mass tumors, non-mass tumors and their combination were ۰.۷۱, ۰.۷۷ and ۰.۷۰, respectively, and by applying it to the combined cluster s LDA and NN-k were ۰.۷۰, ۰.۴۴ and ۰.۶۹, respectively.

کلیدواژه ها:

computer-aided diagnosis ، magnetic resonance imaging ، breast magnetic resonance images ، SVM clustering ، and a combination of LDA and K-NN