Extracting and Selecting Meaningful Features from Mammogram Digital Image Using Factor Analysis Technique
Journal Title: INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY - Year 2013, Vol 11, Issue 5
Abstract
Breast cancer is the most common cancer in women around the world. Various countries including the United Arab Emirates (UAE) offer asymptomatic screening for the disease. The interpretation of mammograms is a very challenges task and is subject  to human error. However the Mammography is considered to be a significant method for detection of breast tumors. Hence, finding an accurate and effective diagnostic method is very important to increase survival rate and reduce mortality of the women. Not all the features in the image give the characteristics and information of the image however, only some extracted features that can express enough information about the image. In this research statistical features selection methods have been developed with association with statistical techniques. 141 ROIs extracted from Digital Database for Screening Mammography (DDSM) has been used to contact this research. Our experiment was classified into two stages in order to reduce the image features which extracted from the ROIs. In the first stage we applied statistical techniques to reduce feature with high accuracy rate, in the second stage we applied graph based method and bayesian inference. Our method was able to achieve high accuracy compared to the original selected features.
Authors and Affiliations
Al Mutaz Mohamed Abdalla, Safaai Bin Deris, Nazar M. Zaki
Keywords
Related Articles
- EP ID EP650322
- DOI 10.24297/ijct.v11i5.1147
- Views 67
- Downloads 0
Semantic link-based Model for User Recommendation in Online community
Recommendation systems have been widely used to overcome the problem of information overloading and help people to make the right decision of needed items, such as: movies, books, products, or even people. This paper pro...
Discovering Mexican Birth Rate Patterns Using Machine Learning Techniques
In this research, we attempt to discover patterns that describe and predict the birth rate in Mexico by using data mining techniques based on relevant demographic and economic information about Mexico. More than twelve m...
MESSAGE GUIDED RANDOM AUDIO STEGANOGRAPHY USING MODIFIED LSB TECHNIQUE
Steganography is the science of hiding secret data in such a way that its presence can't be noticed. Techniques which hide more secret data in cover files and which doesn't affect transparency of cover signal are better...
Fast Iterative model for Sequential-Selection-Based Applications
Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selection problems is presented. This iterative model utilizes an automatic step size calculation that improves the performance...
A Fully Automated Method for Noisy cDNA Microarray Image Quantification
DNA microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. Image processing is an important aspect of microarray experiments...