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Knee disorders are common among the human population. Knee osteoarthritis (OA) is the most widespread knee joint disorder, which may require surgical treatment. The detection and diagnosis of knee joint disorders from medical images demand enormous human effort and time. The development of a computer‐aided diagnosis (CAD) system can notably minimise the burden of medical experts and remove the intra‐observer and inter‐observer variations. To achieve the goal, the highly challenging research problem of knee image segmentation has been frequently paid attention in past years, which can be efficiently applied in the development of the CAD system. Knee image segmentation is a challenging task owing to the image contrasts, intensity variations, shape irregularities, and the presence of thin cartilage structures. Therefore, this paper presents a literature review of automated segmentation approaches mainly focused on the segmentation of knee cartilage and bone, with respect to the underlying technical aspects, datasets used, and the performance reported. The paper also presents the growth from classical segmentation approaches towards the deep learning approaches in the knee image segmentation. Owing to the varying quality and complexity of different knee image datasets, this paper abstains from doing a rigorous comparative evaluation of image segmentation approaches.

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Security is a very important aspect in the biometric system. There are number attacks and there remedial solutions discussed in the literature on different modules of biometrics system and communication links among them. But still the researchers are not able to secure every module of a biometric system against these attacks. Template and database are the very important parts of biometric systems and attacker mostly attack on template and database of biometric system so securing them is a very crucial issue these days. In this research paper our focus is on template and data base security in biometrics system and we develop a system to encrypt and decrypt the biometric image using helper data of a fingerprint and password to make it secure so that even if someone gains access to the encrypted image stored in the database he will not able to reproduce the original image from it and it will be useless for him.