This paper proposed a novel voting ranking random forests (VRRF) method for solving hepatocellular carcinoma (HCC) image classification problem. Firstly, in preprocessing stage, this paper used bilateral filtering for hematoxylin-eosin (HE) pathological images. Next, this paper segmented the bilateral filtering processed image and got three different kinds of images, which include single binary cell image, single minimum exterior rectangle cell image, and single cell image with a size of n⁎n. After that, this paper defined atypia features which include auxiliary circularity, amendment circularity, and cell symmetry. Besides, this paper extracted some shape features, fractal dimension features, and several gray features like Local Binary Patterns (LBP) feature, Gray Level Cooccurrence Matrix (GLCM) feature, and Tamura features. Finally, this paper proposed a HCC image classification model based on random forests and further optimized the model by voting ranking method. The experiment results showed that the proposed features combined with VRRF method have a good performance in HCC image classification problem.
With the need for fast and low-power radiation-hardened processors, advanced technology process is applied to obtain both high performance as well as high reliability. However, scaling down of the size of the transistor makes the transistor sensitive to outside disturbances, such as soft error introduced by the strikes of the cosmic neutron beams. Besides aerospace applications, such reliability should also be taken into consideration for the sub-100[Formula: see text]nm CMOS designs to ensure the robustness of the circuit. In such circumstances, several radiation-hardened flip-flops are designed and simulated under SMIC 40[Formula: see text]nm process. Simulation results show that with five aspects (performance, power, area, PVT variation and reliability) taken into consideration, TSPC-based DICE and TMR combined architecture has the best soft-error robustness in comparison with other radiation-hardened flip-flops, and the critical charge of such architecture is 490[Formula: see text]fC, which is 12.5X higher than the traditional unhardened flip-flop.
We propose a new feature extraction method of liver pathological image based on multispatial mapping and statistical properties. For liver pathological images of Hematein Eosin staining, the image of R and B channels can reflect the sensitivity of liver pathological images better, while the entropy space and Local Binary Pattern (LBP) space can reflect the texture features of the image better. To obtain the more comprehensive information, we map liver pathological images to the entropy space, LBP space, R space, and B space. The traditional Higher Order Local Autocorrelation Coefficients (HLAC) cannot reflect the overall information of the image, so we propose an average correction HLAC feature. We calculate the statistical properties and the average gray value of pathological images and then update the current pixel value as the absolute value of the difference between the current pixel gray value and the average gray value, which can be more sensitive to the gray value changes of pathological images. Lastly the HLAC template is used to calculate the features of the updated image. The experiment results show that the improved features of the multispatial mapping have the better classification performance for the liver cancer.
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