To investigate the effect of image preprocessing, in respect to intensity inhomogeneity correction and noise filtering, on the robustness and reproducibility of the radiomics features extracted from the Glioblastoma (GBM) tumor in multimodal MR images (mMRI). In this study, for each patient 1461 radiomics features were extracted from GBM subregions (i.e., edema, necrosis, enhancement, and tumor) of mMRI (i.e., FLAIR, T1, T1C, and T2) volumes for five preprocessing combinations (in total 116 880 radiomics features). The robustness and reproducibility of the radiomics features were assessed under four comparisons: (a) Baseline versus modified bias field; (b) Baseline versus modified bias field followed by noise filtering; (c) Baseline versus modified noise, and (d) Baseline versus modified noise followed bias field correction. The concordance correlation coefficient (CCC), dynamic range (DR), and interclass correlation coefficient (ICC) were used as metrics. Shape features and subsequently, local binary pattern (LBP) filtered images were highly stable and reproducible against bias field correction and noise filtering in all measurements. In all MRI modalities, necrosis regions (NC: n ̅ ~449/1461, 30%) had the highest number of highly robust features, with CCC and DR >= 0.9, in comparison with edema (ED: n ̅ ~296/1461, 20%), enhanced (EN: n ̅ ~ 281/1461, 19%) and active‐tumor regions (TM: n ̅ ~254/1461, 17%). The necrosis regions (NC: ~ 449/1461, 30%) had a higher number of highly robust features (CCC and DR >= 0.9) than edema (ED: ~ 296/1461, 20%), enhanced (EN: ~ 281/1461, 19%) and active‐tumor (TM: ~ 254/1461, 17%) regions across all modalities. Furthermore, our results identified that the percentage of high reproducible features with ICC >= 0.9 after bias field correction (23.2%), and bias field correction followed by noise filtering (22.4%) were higher in contrast with noise smoothing and also noise smoothing follow by bias correction. These preliminary findings imply that preprocessing sequences can also have a significant impact on the robustness and reproducibility of mMRI‐based radiomics features and identification of generalizable and consistent preprocessing algorithms is a pivotal step before imposing radiomics biomarkers into the clinic for GBM patients.

Unfit drivers are the cause of tens of thousands of incidents on the roads which lead to injuries and deaths. Therefore, it is very important to take preventive measures against such incidents. One of the unfit driving conditions is driving while being drowsy. Using image processing techniques, drowsiness of the driver could be detected and hence such incidents could be prevented. In this work, inspired by how images are processed by the human visual system, an enhancement for driver's drowsiness detection is suggested. Furthermore, to improve the robustness of the drowsiness detection system, the mechanism for using energy levels in frames is changed. Lastly, a better decision making process is proposed. To measure the merit of the system, it is applied to a set of drivers' data. Test results show that using the proposed system, success rate of the drowsiness detection system is 90%.

Signed multiplication is crucial for performing arithmetic operations with positive and negative numbers. It has applications in signal processing, digital image processing, communication, cryptography, neural network hardware accelerators, and more. Until today, to multiply signed numbers, the data are often converted to unsigned format, and the sign is added to the final product. This method imposes high hardware requirements for data conversion and presents challenges in designing approximate multipliers. This paper proposes a novel method for designing approximate signed multipliers that eliminate the need for data conversion, thereby reducing the overall hardware requirement while maintaining accuracy. Comprehensive evaluations at the system level using MATLAB and circuit-level performance analysis using HSPICE demonstrate that the proposed approach offers a successful trade-off between area overhead, power consumption, and accuracy. The achieved trade-off makes the proposed method a promising solution for optimizing digital circuits in various applications such as artificial intelligence and image processing.