Search

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.

Most fingerprint recognition techniques are based on minutiae matching and have been well studied. However, this technology still suffers from problems associated with the handling of poor quality impressions. One problem besetting fingerprint matching is distortion. Distortion changes both geometric position and orientation, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger tip. Marking all the minutiae accurately as well as rejecting false minutiae is another issue still under research. Our work has combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods comes from a wide investigation into research papers. Also some novel changes like segmentation using Morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work., {"references":["A. Jain, R. Bolle, and S. Pankanti, \"Biometrics Personal Identification in\nNetworked Society\", Kluwer Academic Publishers New York, Boston,\nDordrecht, London, Moscow, pp. 1-64, 2002.","A. K. Jain, F. Patrick, A. Arun, \"Handbook of Biometrics\", Springer\nScience+Business Media, LLC, 1st edition, pp. 1-42, 2008.","D. Maio, and D. Maltoni, \"Direct gray-scale minutiae detection in\nfingerprints\", IEEE Transactions Pattern Analysis and Machine\nIntelligence, vol. 19(1), pp. 27-40, 1997.","D. Maltoni, D. Maio, and A. Jain, S. Prabhakar, \"4.3: Minutiae-based\nMethods- (extract) from Handbook of Fingerprint Recognition\",\nSpringer, New York, pp. 141-144, 2003.","E. Hastings, \"A Survey of Thinning Methodologies\", Pattern analysis\nand Machine Intelligence, IEEE Transactions, vol. 4, Issue 9, pp. 869-\n885, 1992.","L. Hong, \"Automatic Personal Identification Using Fingerprints\", Ph.D.\nThesis, 1998.","L. Lam, S. W. Lee, and C. Y. Suen, \"Thinning Methodologies-A\nComprehensive Survey\", IEEE Transactions on Pattern analysis and\nmachine intelligence, vol. 14, no. 9, 1992.","L.C. Jain, U. Halici, I. Hayashi, S.B. Lee, and S. Tsutsui, \"Intelligent\nbiometric techniques in fingerprint and face recognition\", The CRC\nPress, 1999.","K. Nallaperumall, A. L. Fred, and S. Padmapriya, \"A Novel Technique\nfor Fingerprint Feature Extraction Using Fixed Size Templates\", IEEE\n2005 Conference, pp. 371-374, 2005.\n[10] P. Komarinski, P. T. Higgins, and K. M. Higgins, K. Fox Lisa,\n\"Automated Fingerprint Identification Systems (AFIS)\", Elsevier\nAcademic Press, pp. 1-118, 2005."]}