Computer-Aided Diagnosis of Solid Breast Nodules: Use of an Artificial Neural Network Based on Multiple Sonographic Features Segyeong Joo, Yoon Seok Yang, Woo Kyung Moon, and Hee Chan Kim*, Member, IEEE

Skeleton INTRODUCTION INTRODUCTION ULTRASONIC IMAGE DATABASE AND PREPEOCESSING ULTRASONIC IMAGE DATABASE AND PREPEOCESSING FEATURE EXTRACTION FEATURE EXTRACTION NEURAL-NETWORK CLASSIFATION NEURAL-NETWORK CLASSIFATION SYSTEM PERFORMANCE EVALUATION SYSTEM PERFORMANCE EVALUATION RESULTS RESULTS

Introduction definitive benign or malignant sonographic characteristics definitive benign or malignant sonographic characteristics The goal is based on factors The goal is based on factors (1) sonographic features are extracted (1) sonographic features are extracted (2) provide multiple sonographic feature values to the ANN (2) provide multiple sonographic feature values to the ANN

Ultrasonic image database and preprocessing Started with the manually segmented region of interest (ROI) of the lesion area. Started with the manually segmented region of interest (ROI) of the lesion area. Histologically confirmed using Histologically confirmed using (1) core needle biopsy(296) (1) core needle biopsy(296) (2) excisional biopsy(24) (2) excisional biopsy(24) Database include Database include (1) palpable breast lesion (1) palpable breast lesion (2) non-palpable breast lesion (2) non-palpable breast lesion

Preprocessing Preprocessing (1) Median Filtering (2) Unsharp Masking (2) Unsharp Masking (3) Contrast Enhancement (3) Contrast Enhancement (4) Binary Thresholding (4) Binary Thresholding (5) Edge Detection (5) Edge Detection

Feature extraction Determine whether a breast nodule is malignant or benign (1)Spiculation (2)Ellipsoid Shape (3)Branch Pattern (4)Relative Brightness of Nodule (5)Number of Lobulations

Spiculation (1)polar coordinates of boundary pixels of nodule images. (2)maligmant nodule ’ s spiculation consists of alternating hyperechoic straight lines (3) (3)

Ellipsoid shape Ellipsoid shape (1) malignant nodule has taller than wide shape (1) malignant nodule has taller than wide shape (2)

Branch pattern Branch pattern (1) Defined as multiple projections from the nodule within (1) Defined as multiple projections from the nodule within or around ducts extending away from the nipple or around ducts extending away from the nipple (2) number of local extrema in the low-pass-filtered radial (2) number of local extrema in the low-pass-filtered radial distance graph distance graph (3)

Relative brightness of nodule Relative brightness of nodule (1) Malignant nodules are darker when compared with the (1) Malignant nodules are darker when compared with the surrounding surrounding (2) Use thickened the boundary of the image (2) Use thickened the boundary of the image (3) (3)

Number of lobulation Number of lobulation (1) detection of peak value (1) detection of peak value (2) radial distance graph was filtered by a median filter (2) radial distance graph was filtered by a median filter with window size of 30 (about 0.6 rad) and then curve- with window size of 30 (about 0.6 rad) and then curve- fitted to 15th-order polynomials fitted to 15th-order polynomials (3) (3)

Neural-network classification training was stopped when the mean square error became training was stopped when the mean square error became lower than lower than Network topology determination Network topology determination (1)K-fold cross-validation method with k=10 (1)K-fold cross-validation method with k=10 (2)Accuracy is (true_position +true_negative finding)/total finging (2)Accuracy is (true_position +true_negative finding)/total finging

Results Feature extraction Feature extraction

ROC curve (Receiver Operating Characteristic)

ANN classification ANN classification trained ANN showed 100% accuracy for the training set trained ANN showed 100% accuracy for the training set and 91.4% accuracy for the test set. and 91.4% accuracy for the test set. in ROC curve,the sensitivity increased to 99.3% and specificity decreased to 7.3% in ROC curve,the sensitivity increased to 99.3% and specificity decreased to 7.3%

Effect of edge-detection algorithm Effect of edge-detection algorithm 13.2% of the total cases (77/584) were found to be 13.2% of the total cases (77/584) were found to be unacceptable unacceptable After manually corrected boundary data for erroneous After manually corrected boundary data for erroneous cases cases in ROC curve,the sensitivity increased to 99.3% and specificity decreased to 34.7% in ROC curve,the sensitivity increased to 99.3% and specificity decreased to 34.7%

Evaluation of system performance Evaluation of system performance The developed CAD system shows a slightly better result in the performance evaluation study The developed CAD system shows a slightly better result in the performance evaluation study in ROC curve,the sensitivity increased to 99.3% and specificity decreased to 34.7% in ROC curve,the sensitivity increased to 99.3% and specificity decreased to 34.7%

END THANKS EVERYONE