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  • Open Access

    ARTICLE

    Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

    B. V. Santhosh Krishna1, *, T. Gnanasekaran2

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 855-869, 2019, DOI:10.32604/cmc.2019.07585

    Abstract An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction… More >

  • Open Access

    ARTICLE

    A Cryptograph Domain Image Retrieval Method Based on Paillier Homomorphic Block Encryption

    Wenjia Xu1, Shijun Xiang1,*, Vasily Sachnev2

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 285-295, 2018, DOI:10.3970/cmc.2018.01719

    Abstract With the rapid development of information network, the computing resources and storage capacity of ordinary users cannot meet their needs of data processing. The emergence of cloud computing solves this problem but brings data security problems. How to manage and retrieve ciphertext data effectively becomes a challenging problem. To these problems, a new image retrieval method in ciphertext domain by block image encrypting based on Paillier homomophic cryptosystem is proposed in this paper. This can be described as follows: According to the Paillier encryption technology, the image owner encrypts the original image in blocks, obtains the image in ciphertext domain,… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for MRI Brain Tumor Classification

    Ravikumar Gurusamy1, Dr Vijayan Subramaniam2

    CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 91-108, 2017, DOI:10.3970/cmc.2017.053.091

    Abstract A new method for the denoising, extraction and tumor detection on MRI images is presented in this paper. MRI images help physicians study and diagnose diseases or tumors present in the brain. This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis. The ambiguity of Magnetic Resonance (MR) image features is solved in a simpler manner. The MRI image acquired from the machine is subjected to analysis in the work. The real-time data is used for the analysis. Basic preprocessing is performed using various filters for noise removal. The de-noised image is… More >

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