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Red lesions can be regarded as one of the earliest lesions in diabetic retinopathy (DR) and automatic detection of red lesions plays a critical role in diabetic retinopathy diagnosis. In this paper, a novel superpixel Multichannel Multifeature (MCMF) classification approach is proposed for red lesion detection. In this paper, firstly, a new candidate extraction method based on superpixel is proposed. Then, these candidates are characterized by multichannel features, as well as the contextual feature. Next, FDA classifier is introduced to classify the red lesions among the candidates. Finally, a postprocessing technique based on multiscale blood vessels detection is modified for removing nonlesions appearing as red. Experiments on publicly available DiaretDB1 database are conducted to verify the effectiveness of our proposed method.

In this paper, a directional kernel partial least squares (DKPLS) monitoring method is proposed. The contributions are as follows: (1) By analysis of the relevance between the input residual and output variables, the kernel partial least squares (KPLS) residual subspace still contains output-relevant variation. (2) A new KPLS algorithm, DKPLS, is proposed to extract the output-relevant variation. Compared with the conventional algorithm, the DKPLS algorithm builds a more direct relationship between the input and output variables. (3) On the basis of the proposed DKPLS algorithm, a process monitoring method is proposed. In this monitoring method, kernel latent variables are used to explain the extracted output-relevant variation and calculate monitoring indices. Faults are detected accurately by the proposed DKPLS method. The DKPLS monitoring method is used to monitor a numerical example and the electrofused magnesium process. The experimental results show the effectiveness of the proposed DKPLS method.

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Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

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