Studies have shown that tumor angiogenesis is an essential process for tumor growth, proliferation and metastasis. Also, tumor angiogenesis is an important prognostic factor of clear cell renal cell carcinoma (ccRCC), as well as a factor in guiding treatment with antiangiogenic agents. Here, we attempted to find the associations between tumor angiogenesis and radiomic imaging features from PET/MRI. Specifically, sparse canonical correlation analysis was conducted on 3 feature datasets (i.e., radiomic imaging features, tumor microvascular density (MVD), and vascular endothelial growth factor (VEGF) expression) from 9 patients with primary ccRCC. In order to overcome the potential bias of intratumoral heterogeneity of angiogenesis, this study investigated the relationship between regional expressions of angiogenesis and VEGF, and localized radiomic features from different parts within the tumors. Our study highlighted the significant strong correlations between radiomic features and MVD, and also demonstrated that the spatiotemporal features extracted from DCE-MRI provided stronger radiomic correlation to MVD than the textural features extracted from Dixon sequences and FDG PET. Furthermore, PET/MRI, which takes advantage of the combined functional and structural information, had higher radiomics correlation to MVD than solely utilizing PET or MRI alone.

An adaptive approach to select analysis window parameters for linear frequency modulated (LFM) signals is proposed to obtain the optimal 3 dB signal-to-noise ratio (SNR) in the short-time Fourier transform (STFT) domain. After analyzing the instantaneous frequency and instantaneous bandwidth to deduce the relation between the window length and deviation of the Gaussian window, high-order statistics is used to select the appropriate window length for STFT and get the optimal SNR with the right time-frequency resolution according to the signal characteristic under a fixed sampling rate. Computer simulations have verified the effectiveness of the new method.

In this letter, a novel and effective algorithm for the low-light image enhancement is proposed based on Retinex model, named Blind Adaptive Structure-Preserving Image Enhancement (BASSY). The low-light image enhancement is still a challenging task because the decomposition of images into light components and reflection components is an ill-posed problem. BASSY adopts a content-adaptive guided filtering based on local variances to estimate the proper illumination map. The salient features of the proposed approach are: (1) For the illumination component, the overall structure in the low-light image is preserved and the texture details are smoothed. (2) The reflectance is estimated without logarithmic transformation to reduce the computational burden and to avoid over-smoothing the reflectance component. (3) The adaptive gamma correction for the illumination map is used to reconstruct the enhanced image. (4) BASSY can be implemented efficiently due to the low computation complexity (N). Experimental results on six public datasets show that the enhanced images by the BASSY exhibit higher naturalness and better visual quality than six state-of-the-art methods.