Relevance vector machine for automatic detection of clustered microcalcifications

Abstract

Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine (RVM)--for detection of MCs in digital mammograms. RVM is based on Bayesian estimation theory, of which a distinctive feature is that it can yield a sparse decision function that is defined by only a very small number of so-called relevance vectors. By exploiting this sparse property of the RVM, we develop computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms. We formulate MC detection as a supervised-learning problem, and apply RVM as a classifier to determine at each location in the mammogram if an MC object is present or not. To increase the computation speed further, we develop a two-stage classification network, in which a computationally much simpler linear RVM classifier is applied first to quickly eliminate the overwhelming majority, non-MC pixels in a mammogram from any further consideration. The proposed method is evaluated using a database of 141 clinical mammograms (all containing MCs), and compared with a well-tested support vector machine (SVM) classifier. The detection performance is evaluated using free-response receiver operating characteristic (FROC) curves. It is demonstrated in our experiments that the RVM classifier could greatly reduce the computational complexity of the SVM while maintaining its best detection accuracy. In particular, the two-stage RVM approach could reduce the detection time from 250 s for SVM to 7.26 s for a mammogram (nearly 35-fold reduction). Thus, the proposed RVM classifier is more advantageous for real-time processing of MC clusters in mammograms.

Hyperspectral Image Classification Using Relevance Vector Machines

“…It is shown in [17] that the training time of RVM is about seven to eight times longer than that of SVM, whereas the testing time of RVM is about seven to eight times shorter than SVM. It is however noted in [16] that the increased training time of RVM is significantly offset by the lack of necessity to perform cross validation over nuisance parameters.…”

“…Relevance vector machine (RVM)-based regression and classification have been proposed in [15]- [17]. The advantages of the RVM over the SVM are probabilistic predictions, automatic estimations of parameters, and the possibility of choosing arbitrary kernel functions [15], [16].…”

“…Hence, classification can be carried out much Manuscript faster with the RVM compared to the SVM. For example, in [17], the RVM has been used for the detection of microcalcification clusters in digital mammograms, and it has been shown that the RVM classifier is much more suitable for real-time processing and reduces the computational complexity compared to SVM-based classification, while maintaining similar detection accuracy.…”

Applying wavelets transform, rough set theory and support vector machine for copper clad laminate defects classification

“…SVMs (Chapelle, Haffner, & Vapnik, 1999;El-Naqa, Yongyi, Wernick, Galatsanos, & Nishikawa, 2002;Kim, Jung, Park, & Kim, 2002;Kim, Jung, & Kim, 2003;Liyang, Yongyi, Nishikawa, & Wernick, 2005a;Liyang, Yongyi, Nishikawa, & Yulei, 2005b;Song, Hu, & Xie, 2002;Vapnik, 1995) have recently been proposed as popular tools for learning from experimental data. The reason is that SVMs are much more effective than other conventional nonparametric classifiers (e.g., the neural networks, nearest neighbor, k-NN classifier) in term of classification accuracy, computational time, stability to parameter setting.…”

Gaussian Mixture Model Based Classification of Microcalcification in Mammograms Using Dyadic Wavelet Transform

“…The input pattern to the SVM classifier is a small pixel window placed centered at the location of interest. Wei et al (2005a) implemented an algorithm based on Relevance Vector Machine (RVM) for the detection of MCs in digital mammograms. RVM classifier could greatly reduce the computational complexity.…”