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Semi-parametric modification of cumulative sum algorithms for the change-point detection of non-Gaussian sequences

Zabolotnii S. W, Warsza Z. L.

Measurement Automation Monitoring

2015

Vol. 61, No. 12

532--534

The expansion of logarithm likelihood ratio in the stochastic series to find the sequential change-point detection of non-Gaussian sequences is used. The moment criteria of the minimum of upper limit error probabilities sum to find the expansion coefficients is applied. The proposed method is a semi-parametric type of cumulative sum (CUSUM) algorithm which needs of higher-order statistics. Results show that polynomial algorithms are more effective in comparison with similar non-parametric procedures.

Detection of Synthetic and Real Microcalcifications based on Statistical Analysis of Original and Highpass-Filtered Mammograms

Abdel-Qader I., Zyout I., Jacobs C.

Machine Graphics and Vision

2009

Vol. 18, No. 3

267-288

Detection of clustered microcalcifications in digitized mammograms can be very useful for early detection of breast cancer. Clustered microcalcifications have a distinguished signature in both spatial and frequency domains. In the spatial domain, they appear as white spots which represent local maxima, while in the frequency domain microcalcifications represent local anomalies that can be captured within the high frequency subbands. In this work, we propose an algorithm for detection of clustered microcalcifications by utilizing these signatures, integrating the statistical parameters of both spatial and frequency domains. The results prove the effectiveness of the proposed method, and indicate that the exploitation of both domain signatures of the clustered microcalcifications yields significantly better detection results.