Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.
The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.
The establishment of a secure communication between two communicating parties is becoming a difficult problem due to the likelihood of attacks and other unintentional changes during an active communication over an unsecured network. However, the security of secret information can be secured using either cryptography or steganography. Steganography refers to the practice of concealing a message (with no traceability) in a manner that it will make no meaning to anyone else except the intended recipient, while cryptography, on the other hand, refers to the art of converting a plaintext (message) into an unreadable format. Thus, steganography conceals the existence of a secret message while cryptography alters the message format itself. Both steganographic and cryptographic techniques are powerful and robust. In this paper, the major aim is to review several ways of combining steganographic and cryptographic techniques to achieve a hybrid system. Moreover, some of the differences between cryptographic and steganographic techniques were presented as well.
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