This paper presents an approach for detecting microcalcifications in digital mammograms employing wavelet-based subband image decomposition. The microcalcifications appear in small clusters of few pixels with relatively high intensity compared with their neighboring pixels. These image features can be preserved by a detection system that employs a suitable image transform which can localize the signal characteristics in the original and the transform domain. Given that the microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands, suppressing the low-frequency subband, and, finally, reconstructing the mammogram from the subbands containing only high frequencies. Preliminary experiments indicate that further studies are needed to investigate the potential of wavelet-based subband image decomposition as a tool for detecting microcalcifications in digital mammograms.

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As a commonly used Chinese herbal medicine in clinic, LongGu is often used as a powder, which is difficult to identify manually. In this study, unsupervised learning method was used to model and identify LongGu, calcining LongGu and LongGu counterfeits by infrared spectroscopy. After preprocessing the original data, the current commonly used principal component analysis method, which was used to reduce the data dimension, and then the KMeans algorithm was used to realize the classification and identification of the samples. The classification results were better in the theoretical samples, but not in the actual samples. Based on this problem, this paper proposes a convolution-based machine learning feature dimension reduction method for spectral data. Comparing with the data method of principal component analysis, the partial classification effect is obviously better on the theoretical data. The effect of identifying calcining LongGu and LongGu counterfeits in actual data has been significantly improved, and it also provides a reference for the application of machine learning technology in the field of spectral identification of traditional Chinese medicine.