Studies on Applications of Computer-Aided Diagnosis to Breast Ultrasound
Date Issued
2007
Date
2007
Author(s)
Yang, Hsin-Chia
DOI
zh-TW
Abstract
Breast cancer is a leading cause of cancer death among women no matter in Europe, America or Taiwan. Thus, early diagnosis and treatment is necessary. Breast ultrasound beats other modalities since it is non-invasive, no ionizing radiation, able to use in dense breasts, and so on. However, it suffers from the problems of operator dependency and limited image contrast. The former can be reduced by computer-aided diagnosis (CAD), a technique using quantitative features to help interpretation. The latter can be solved by methods such as parametric imaging, adaptive imaging and high frequency ultrasound. It is known that acoustic features can reflect tissue properties, adaptive imaging can reduce distortion, and high frequency ultrasound can enhance spatial resolution. Therefore, if we can combine these methods with CAD, the diagnostic ability of breast ultrasound is supposed to be improved definitely. For this reason, we plan to investigate to what extent acoustic features, adaptive imaging and high frequency ultrasound can influence the performance of CAD through an unconventional experimental setup.
In this computerized method, the acoustic features were relative sound velocity and relative attenuation coefficient (to the background). They were reconstructed using limited-angle ultrasonic computed tomography with an imaging setup consisting of a digital phased array system (DiPhAS, Fraunhofer IBMT, St. Ingbert, Germany), a linear array (L6/128, STI, State College, PA, USA) with 128 channels, a center frequency of 5.57 MHz, and a metal plate reflector to acquire B-mode images, time-of-flight data, and attenuation data at the same time. The image texture features were autocorrelation coefficient, average brightness, and parameters derived from gray level concurrence matrix and from non-separable wavelet transform. The image morphological feature were depth-to-width ratio and normalized radial gradient. The dataset was trained and classified by a linear support vector machine, validated by leave-one-out method, and evaluated by the area under receiver operating characteristic (ROC) curve (Az). In adaptive imaging, a method termed generalized coherence factor (GCF) was used to reduce the focusing errors from tissue inhomogeneities. In high frequency imaging, chirp waveform with a center frequency of 25 MHz was transmitted and a lithium-niobate single-crystal focused transducer (Onda Corporation, Sunnyvale, CA, USA) was used.
Maybe because of the calculation errors, insufficient sample size, and the low signal to noise ratio, though the linear correlation coefficients (CC) between acoustic, morphological and texture features were relatively low, the combination of both of them for classification did not necessarily improve the discrimination ability in the CAD. Adaptive imaging was more benefit to the features related to linearity in discrimination maybe because of the improvement in contrast resolution so that the lines can be more obvious. However, this contrarily made the features related to homogeneity, brightness variation, and brightness perform worse in discrimination since formerly homogeneous benign images became significant in brightness variation as the characteristic of malignant images. High frequency ultrasound was more benefit to the features related to homogeneity in classification partly because of the high spatial resolution. However, maybe due to the more significant speckles in high frequency ultrasound, the features related to randomness and brightness variation performed worse in classification.
In summary, this thesis evaluates the performance of acoustic features, high frequency ultrasound and adaptive imaging on CAD. It turns out that these methods did not necessarily improve the discrimination ability, even though it has been taken for granted that they can make the diagnostic performances better.
Subjects
電腦輔助診斷
乳房超音波
聲學特徵
紋路分析
有限角度超音波電腦斷層掃描
可適性影像
computer-aided diagnosis (CAD)
breast ultrasound
acoustic feature
texture analysis
limited-angle ultrasonic computed tomography
high frequency ultrasound
adaptive imaging
SDGs
Type
thesis
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