Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms. In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast. This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing the number of the suspicious regions. The proposed method provides fully-automatic segmentation of the suspicious regions. This method is based on determining an adaptive multi-threshold value by using three different techniques together. These techniques are Otsu multilevel thresholding, Havrda & Charvat entropy, and w-BSAFCM algorithm that was developed by the authors of this paper for image clustering applications. In the proposed method, segmentation of a mammogram is performed on two sub-images obtained from that mammogram, the pectoral muscle and the breast region to prevent any information loss. The method was tested on 55 mass-mammograms and 210 non-mass mammograms of the mini-MIAS database, and it was compared with Shannon, Renyi, and Kapur entropy methods and with some of the related studies from the literature. The segmentation results of the tests were evaluated in terms of the number of suspicious regions, the number of correctly detected masses, and the performance measure parameters, accuracy, false-positive rate, specificity, volumetric overlap, and dice similarity coefficient. According to the evaluations, it was shown that the proposed method can both successfully locate the mass regions and significantly reduce the number of the non-mass suspicious regions on the mammograms.

Open access peer-reviewed chapter

Despite the fact that chaotic systems do not have very complex circuit structures, interest in chaotic systems has increased considerably in recent years due to their interesting dynamic properties. Thanks to the noise-like properties of chaotic oscillators and the ability to mask information signals, great efforts have been made in recent years to develop chaos-based TRNG structures. In this study, a new chaos-based dual entropy core TRNG with high operating frequency and high bit generation rate was realized using 3D Pehlivan-Wei Chaotic Oscillator (PWCO) structure designed utilizing RK-Butcher numerical algorithm on FPGA and ring oscillator structure. In the FPGA-based TRNG model of the system, 32-bit IQ-Math fixed-point number standard is used. The developed model is coded using VHDL. The designed TRNG unit was synthesized for Virtex-7 XC7VX485T-2FFG1761 chip produced by Xilinx. Then, the statistics of the parameters of FPGA chip resource usage and unit clock speed were examined. The data processing time of the TRNG unit was achieved by using the Xilinx ISE Design Tools 14.2 simulation program, with a high bit production rate of 437.043 Mbit/s. In addition, number sequences obtained from FPGA-based TRNG were subjected to the internationally valid statistical NIST 800-22 Test Suite and all the randomness tests of NIST 800-22 Test Suite were successful.

While there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and contracts. In this study, the aim was to design network architectures that work very fast in areas that require only signature images. For this purpose, a new Si-CNN network architecture with existing layers was designed. Afterwards, a new loss function and layer (Si-CL), a novel architecture using Si-CL as classification layer in Si-CNN to increase the performance of this architecture, was designed. This architecture was called Si-CNN+NC (New Classification). Si-CNN and Si-CNN+NC were trained with two datasets. The first dataset which was used for training is the “C-Signatures” (Classification Signatures) dataset, which was created to test these networks. The second dataset is the “Cedar” dataset, which is a benchmark dataset. The number of classes and sample numbers in the two datasets are symmetrical with each other. To compare the performance of the trained networks, four of the most well-known pre-trained networks, GoogleNet, DenseNet201, Inceptionv3, and ResNet50, were also trained with the two datasets with transfer learning. The findings of the study showed that the proposed network models can learn features from two different handwritten signature images and achieve higher accuracy than other benchmark models. The test success of the trained networks showed that the Si-CNN+NC network outperforms the others, in terms of both accuracy and speed. Finally, Si-CNN and Si-CNN+NC networks were trained with the gold standard dataset MNIST and showed superior performance. Due to its superior performance, Si-CNN and Si-CNN+NC can be used by signature experts as an aid in a variety of applications, including criminal detection and forgery.