Statistical Texture Model for mass Detection in Mammography

A Texture-Based Statistical Model for Face Detection

Texture is an important cue for detecting objects that undergo shape deformation, pose changes and variations in illumination. We propose a general statistical model which relies on texture for learning an object class from a set of example images. We use the class of human faces to test our ideas. Once the model learns the distribution of the face images, it can then be used to classify new im...

A Hybrid Method for Mammography Mass Detection Based on Wavelet Transform

Introduction:  Breast  cancer  is  a  leading  cause  of  death  among  females  throughout  the  world.  Currently,  radiologists are able to detect only 75% of breast cancer cases. Making use of computer-aided design (CAD)  can play an important role in helping radiologists perform more accurate diagnoses.   Material and Methods: Using our hybrid method, the background and the pectoral muscle...

Computer-aided mass detection in mammography: false positive reduction via gray-scale invariant ranklet texture features.

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Seco...

Automatic Detection of Mass Type Breast Cancer using Texture Analysis in Korean Digital Mammography

In this study, we present an advanced detection technique for mass type breast cancer based on texture information of organs. The proposed method detects the cancer areas in three stages. In the first stage, the midpoints of mass area are determined based on AHE (Adaptive Histogram Equalization). In the second stage, we set the threshold coefficient of homogeneity by using MLE (Maximum Likeliho...

Statistical Models for the Detection of Abnormalities in Digital Mammography