title: Duct detection and wall spacing estimation in breast tissue.
abstract: The relationship between duct tissue and several types of malignant disease suggests that methods for characterizing duct structures may be useful tools in ultrasonic tissue characterization. This paper presents performance results from ultrasonic phantom experiments and Monte Carlo simulations for detecting and estimating duct wall spacings on the order of those typically found in breast tissue using methods based on the generalized spectrum (GS) and cepstrum. A performance comparison demonstrates the advantages of each method and examines the effects of various signal processing options, including a special normalization technique for the GS that effectively whitens the data spectrum and reduces interfering spectral influences with little overall performance loss. Experimental results (for both simulation and phantom) indicate that the GS typically achieves detection rates of over 90% (at 10% false alarm rates) over a broad range of SNR values (3-21 dB). The GS detection performance exceeds that of the cepstrum and exhibits more robustness to noise and signal processing parameters. Simulation results with fixed system effects indicate better estimation performance for cepstral-based methods, while experimental phantom results show the GS estimation performance to be the same or better than the cepstral-based method.
authors: Electrical Engineering Department, University of Kentucky, College of Engineering, Lexington 40506, USA.
authors: L
authors: L
authors: Huang
authors: K D
authors: KD
authors: Donohue
authors: V
authors: V
authors: Genis
authors: F
authors: F
authors: Forsberg
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging* / Q000473:pathology; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D016217:Ultrasonography, Mammary / Q000379:methods*