Shape-based and texture-based feature extraction for classification of microcalcifications in mammograms

Hamid Soltanian-Zadeh, Siamak Pourabdollah-Nezhad, Farshid Rafiee Rad, Kenneth M. Hanson, Milan Sonka
2001 Medical Imaging 2001: Image Processing  
This paper presents and compares two image processing methods for differentiating benign from malignant microcalcifications in mammograms. The gold standard method for differentiating benign from malignant microcalcifications is biopsy, which is invasive. The goal of the proposed methods is to reduce rate of biopsies with negative results. In the first method, we extract 17 shape features from each mammogram. These features are related to shapes of individual microcalcifications or to their
more » ... ons or to their clusters. In the second method, we extract 44 texture features from each mammogram using co-occurrence method of Haralick. Next, we select best features from each set using a genetic algorithm, to maximize area under ROC curve. This curve is created using a k-nearest neighbor (kNN) classifier and a malignancy criterion. Finally, we evaluate the methods by comparing ROC's with greatest areas obtained using each method. We applied the proposed methods, with different values of k in kNN classifier, to 74 malignant and 29 benign microcalcification clusters. Truth for each mammogram was established based on the biopsy results. We found greatest area under ROC curve for each set of features used in each method. For shape features this area was 0.82 (k = 7) and for Haralick features it was 0.72 (k = 9). ∆ x and ∆ y , and the four angles o 0 , 45 o , o 90 and o 135 are defined for calculating the matrix for each of the 4 distances. This makes 16 matrices for each cluster. We calculate the 13 SGLDM features for each of these matrices. These features are named as follows: angular second moment, contrast, correlation, variance, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, and two information measures of correlation. Due to
doi:10.1117/12.431100 dblp:conf/miip/Soltanian-Zadeh01 fatcat:4xccu3zjsnfbbhnpwmnqc4fn4i