Mammograms Of Interval-cancer Cases Research Articles

Architectural Distortion Detection in Mammogram using Contourlet Transform and Texture Features

Breast Cancer is one of the most affecting diseases in the world. Architectural Distortion is one of the indications of breast cancer. It is an abnormal arrangement of tissue strands of the breast, often a radial or perhaps a somewhat random pattern, but without any associated mass as the apparent cause of this distortion. This project used Contourlet Transform based method to detect the location of the architectural distortion in mammograms. The dominant angle is detected in this Contourlet decomposition which comprises of Laplacian Pyramidal Filter bank and Steerable Gaussian Filter. The textural features will not show any deviation in the distorted area. Here the methods proposed will assist the Radiologist to identify the unidentified potential sites of distortion. Therefore the main aim of this work becomes the reduction of the region of interests. In this project 39 cases were testes in which 19 are Architectural Distorted image from the MIAS database and 17 Normal images. The proposed algorithm detected 1502 regions of interest (ROIs) with 247 True Positives and 1255 False Positives. In this paper 26 textural features were considered and result is obtained as 0.77 with an artificial neural network. The proposed methods have significant result in detecting the architectural distortion in mammograms of interval cancer cases.

Computer-aided detection of architectural distortion in prior mammograms of interval cancer.

Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick's texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases.