Search Results - (( developing code classification algorithm ) OR ( image segmentation method algorithm ))

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  1. 1

    Improving brain tumor segmentation in MRI images through enhanced convolutional neural networks by Ayomide, Kabirat Sulaiman, Mohd Aris, Teh Noranis, Zolkepli, Maslina

    Published 2023
    “…The research focuses on enhancing brain tumor segmentation in MRI images by using Convolutional Neural Networks and reducing training time by using MATLAB's GoogLeNet, anisotropic diffusion filtering, morphological operation, and sector vector machine for MRI images. …”
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    Article
  2. 2

    UNSUPERVISED SEGMENTATION OF CORAL REEF IMAGES BY USING COLOR AND TEXTURE FEATURES by MOHAMMAD SAMEER ALOUN

    Published 2022
    “…This thesis presents new methods to automate the segmentation of underwater coral reef images based on image processing techniques with the combination of color-texture features. …”
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    Thesis
  3. 3

    Breast Ultrasound Automated ROI Segmentation with Region Growing by Lee, Lay-Khoon, Liew, Siau-Chuin

    Published 2015
    “…One of the simplest methods for image segmentation that is widely implemented in medical images is the region growing method. …”
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  4. 4

    Image segmentation based on normalised cuts with clustering algorithm by Choong, Mei Yeen

    Published 2013
    “…With the approach applied in the normalised cuts based image segmentation, the constraint of using normalised cuts algorithm in image segmentation can be alleviated. …”
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    Thesis
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    Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation by Alia, Osama Moh’d Radi

    Published 2011
    “…Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. …”
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    Thesis
  7. 7

    Fuzzy modeling of brain tissues in Bayesian segmentation of brain MR images by Farzan, Ali, Ramli, Abdul Rahman, Mashohor, Syamsiah, Mahmud, Rozi

    Published 2010
    “…Segmentation of brain MRI is the core part in plenty of medical image processing methods. …”
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  8. 8

    Biometric identification and recognition for iris using failure rejection rate (FRR) / Musab A. M. Ali by M. Ali, Musab A.

    Published 2016
    “…Hence, the objectives of this research are new algorithms development significantly for iris segmentation specifically the proposed Fusion of Profile and Mask Technique (FPM) specifically in getting the actual center of the pupil with high level of accuracy prior to iris localization task, followed by a particular enhancement in iris normalization that is the application of quarter size of an iris image (instead of processing a whole or half size of an iris image) and for better precision and faster recognition with the robust Support Vector Machine (SVM) as classifier. …”
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    Thesis
  9. 9

    Segmenting nodules of lung tomography image with level set algorithm and neural network by Chong, Soon Yee, Tan, Min Keng, Yeo, Kiam Beng, Mohd Yusof Ibrahim, Hao, Xiaoxi, Teo, Kenneth Tze Kin

    Published 2019
    “…The aim of this research is to develop an image segmentation algorithm for nodule detection in computed tomography (CT) image. …”
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    Proceedings
  10. 10

    Image enhancement and segmentation on simultaneous latent fingerprint detection by Rozita, Mohd Yusof

    Published 2015
    “…Segmentation of 240 images is performed and compared with manual segmentation methods. …”
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    Thesis
  11. 11

    Image storage and retrieval using color quantization method by Mandal, Koushik Kanti, Islam, Md. Rafiqul, Md. Sap, Mohd. Noor, Khatun, Fahmida, Reza, Md. Parvez

    Published 2005
    “…The image is segmented by our proposed segmentation algorithm, which uses the compact color descriptor .The result of the experiment shows that the developed segmentation algorithm produces better performances as compared with the existing segmentation algorithm. …”
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    Article
  12. 12

    A Hybrid Active Contour and Artificial Bee Colony Algorithm for Segmenting Mixed-Meal Food Images (S/O: 13239) by Harun, Nor Hazlyna, Mahmuddin, Massudi, Harun, Hazaruddin

    Published 2020
    “…This method is adopted for food image segmentation, wherein the boundaries of the foods in an image are the objects of interest. …”
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    Monograph
  13. 13

    Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation by Li, Min, Huang, Tinglei, Zhu, Gangqiang

    Published 2008
    “…Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. …”
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    Article
  14. 14

    Enhanced Clustering Algorithms For Gray-Scale Image Segmentation by Siddiqui, Fasahat Ullah

    Published 2012
    “…The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. …”
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    Thesis
  15. 15

    Using a novel algorithm in ultrasound images to detect renal stones by Sania Eskandari, Saeed Meshgini, Ali Farzamnia

    Published 2021
    “…In this paper, three essential segmentation algorithms, namely Fuzzy C-means, K-means, and Expectation–Maximization algorithms, are proposed for the identification of renal stone in kidney ultrasound images. …”
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    Proceedings
  16. 16

    Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm by Abdurrahim, Salem Hamed

    Published 2004
    “…The watershed segmentation algorithm is a very powerful segmentation tool, but it also has difficulty in segmenting MR images due to noise and shading effect present. …”
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    Thesis
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    Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs by Soo , Say Leong

    Published 2006
    “…Finally. a defect segmentation algorithm was developed to segment the defects automatically from the image using background subtraction and rank leveling method.…”
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    Thesis
  20. 20

    MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm by Balafar, Mohammad Ali, Ramli, Abdul Rahman, Saripan, M. Iqbal, Mahmud, Rozi, Mashohor, Syamsiah, Balafar, Hakimeh

    Published 2008
    “…Image segmentation is a critical stage in many computer vision and image process applications. …”
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    Conference or Workshop Item