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Author: search.filters.author.S, KeerthanaHas files: Yes

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    A BRIEF STUDY OF IMAGE PROCESSING AND TECHNIQUES
    (CiiT International Journal of Digital Image Processing, 2017-02) S, Keerthana; K, Sathiyakumari
    The development of digital image processing is closely tied to the development of the digital computers. Because of its nature, digital image requires lot of storage space and their processing needs so much computational power that progress in the field of digital image processing had been highly dependent on the development of modern digital computer which came only in 1940s. This paper is a complete review of various image processing techniques and large number of related application in diverse disciplines, including medical, biometrics, moving object tracking, vehicle detection & monitoring, document analysis and retrieval, outdoor surveillance, remote sensing and Traffic queue detection algorithm for processing various real time image processing challenges. Techniques discussed segmentation, edge detection and corner detection also application areas and their future scope are explained. The intension of this paper is useful to researchers and practitioners interested in real time image processing.
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    BRAIN STROKE SEGMENTATION USING FUZZY C-MEANS CLUSTERING
    (Foundation of Computer Science, 2016-11) S, Keerthana; K, Sathiyakumari
    Image processing technique plays an important role in medical science for envisage various phenomenal structure of human body. Even though it helps more, sometimes it’s very difficult to detect abnormal structures of human body by using simple images. Magnetic Resonance Imaging (MRI) is the one of the most significant technique to analyze human body and helpful for distinguishing and expounding the neural architecture of human brain effectively. This proposed strategy focus on detection and extraction of brain stroke from different patient’s MRI images. In this work some preprocessing techniques like noise removal, filtering and segmentation is used for extract brain stroke partition accurately. The segmentation of brain stroke is implemented by using Fuzzy C-Means (FCM) clustering with two different levels of extraction. Edge detection is used for finding segmented portion of brain stroke edges accurately. Finally the stroke size is calculated for help doctors to make effective decisions about brain stroke. The experimental result proven that the proposed method is successful in detecting and extraction brain stroke efficiently with less time.
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    SUPERVISED LEARNING APPROACH FOR BRAIN STROKE CLASSIFICATION USING DEEP LEARNING TECHNIQUES
    (International Journal of Research in Engineering and Technology, 2016-10) S, Keerthana; K, Sathiyakumari
    This research present the detection and segmentation of brain stroke using fuzzy c-means clustering and H2O deep learning algorithms. Brain stroke segmentation in magnetic resonance imaging (MRI) has become an evolving research area in the field of a medical imaging system. Brain stroke detection helps in finding the exact size, shape, extraction and location of the stroke. The system is consisting of three stages to detect and segment a brain stroke. An efficient algorithm is proposed for stroke detection based on segmentation and preprocessing techniques. The firstly quality of a scanned image is enhanced and then preprocessing techniques are applied to detect the stroke in the scanned image. In this system film artifacts removal, skull extraction and filtering methods are used to enhance the image. The second stage preprocessed image is segmented using fuzzy c-means clustering to obtain stroke region and edges are detected for accurate prediction of stroke location. After that edge detection operator is applied for boundary extraction and to find the size of the stroke, which helps doctors to make a decision about stroke location, size and etc. Finally, H2O deep learning method is used to classify stroke based on texture features and statistical features. The experimental result shows that the proposed work is performed well in detecting brain stroke efficiently.