Classification of breast ultrasound images using fractal feature.
Fractal analyses have been applied successfully for the image compression, texture analysis, and texture image segmentation. The fractal dimension could be used to quantify the texture information. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Furthermore, a computer-aided diagnosis (CAD) system based on the fractal analysis is proposed to classify the breast lesions into two classes: benign and malignant. To improve the classification performances, the ultrasound images are preprocessed by using morphology operations and histogram equalization. Finally, the k-means classification method is used to classify benign tumors from malignant ones. The US breast image databases include only histologically confirmed cases: 110 malignant and 140 benign tumors, which were recorded. All the digital images were obtained prior to biopsy using by an ATL HDI 3000 system. The receiver operator characteristic (ROC) area index AZ is 0.9218, which represents the diagnostic performance.
Chen
Dar-Ren
DR
Department of General Surgery, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua 500, Taiwan. dlchen88@ms13.hinet.net
Chang
Ruey-Feng
RF
Chen
Chii-Jen
CJ
Ho
Ming-Feng
MF
Kuo
Shou-Jen
SJ
Chen
Shou-Tung
ST
Hung
Shin-Jer
SJ
Moon
Woo Kyung
WK
D000293:Adolescent; D000328:Adult; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005260:Female; D017709:Fractals; D006801:Humans; D008433:Mathematics; D008875:Middle Aged; D012372:ROC Curve; D016217:Ultrasonography, Mammary