Essay
Semi-Supervised K-Means Clustering for Outlier
Detection in Mammogram Classification
K. Thangavel1, A. Kaja Mohideen2
Department of Computer Science, Periyar University, Salem, India
1
drktvelu@yahoo.com, 2kaja.akm@gmail.com
Abstract— Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the number of clusters is equal with the number of classes. The clusters are compared with original classes, the wrongly clustered instances are identified as outliers and they are removed from the feature space. A novel Genetic Association Rule
Miner (GARM) is applied with this reduced feature set to construct the association rules for classification. The performance is analyzed with rough set using Receiver
Operating Characteristic (ROC) curve analysis. The mammogram images from MIAS (Mammogram Image
Analysis Society) and DDSM (Digital Database for
Screening Mammography) were used to evaluate the performance. K eywords- Mammogram; k-Means Clustering; Shape Features;
Outlier Detection.
I.
INTRODUCTION
Mammography is currently the most effective imaging modality for breast cancer screening. Computer Aided (CA) diagnosis systems have been developed to aid radiologists in detecting mammographic lesions, characterized by promising performance [1]. Various CA diagnosis algorithms have been proposed for the characterization of microcalcifications (MCs), an important indicator of malignancy [2-4]. These algorithms are based on extracting image features from regions of interest
(ROIs) and estimating the probability of malignancy for a given MC cluster. A variety of computer-extracted features and classification schemes have been used to automatically
Detection in Mammogram Classification
K. Thangavel1, A. Kaja Mohideen2
Department of Computer Science, Periyar University, Salem, India
1
drktvelu@yahoo.com, 2kaja.akm@gmail.com
Abstract— Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the number of clusters is equal with the number of classes. The clusters are compared with original classes, the wrongly clustered instances are identified as outliers and they are removed from the feature space. A novel Genetic Association Rule
Miner (GARM) is applied with this reduced feature set to construct the association rules for classification. The performance is analyzed with rough set using Receiver
Operating Characteristic (ROC) curve analysis. The mammogram images from MIAS (Mammogram Image
Analysis Society) and DDSM (Digital Database for
Screening Mammography) were used to evaluate the performance. K eywords- Mammogram; k-Means Clustering; Shape Features;
Outlier Detection.
I.
INTRODUCTION
Mammography is currently the most effective imaging modality for breast cancer screening. Computer Aided (CA) diagnosis systems have been developed to aid radiologists in detecting mammographic lesions, characterized by promising performance [1]. Various CA diagnosis algorithms have been proposed for the characterization of microcalcifications (MCs), an important indicator of malignancy [2-4]. These algorithms are based on extracting image features from regions of interest
(ROIs) and estimating the probability of malignancy for a given MC cluster. A variety of computer-extracted features and classification schemes have been used to automatically
References: Video Processing, 2nd ed., A. C. Bovik Ed. Academic Press, 2005, pp. 2, pp: 233-235, 2009.