Research Publications

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    An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm
    (IEEE, 2022-08-03) Rathnayake, N; Mampitiya, L. I
    This research study is focused on the classification of ocular diseases by referring to a well-known dataset. The data is divided into seven classes: diabetes, glaucoma, cataract, normal, hypertension, age-related macular degeneration, pathological myopia, and other diseases/abnormalities. A Neural Network is used for the classification of diseases. In addition, the GLCM and LBP feature extracting methods have been used to carry out the feature extraction for the fundus images. This study compares five different ocular disease recognizing techniques. Moreover, the proposed model was evaluated regarding precision, recall, and accuracy. The proposed solution outperformed existing state-of-the-art algorithms, achieving 99.58% accuracy.
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    Feature Descriptor for Sri Lankan Batik Patterns Using Hu Moment Invariants and GLCM
    (IEEE, 2021-08-11) Senarathna, B. P. H. K. M. D; Rajakaruna, T
    Batik is a traditional craft of designing patterned fabrics which hold high artistic value in Sri Lankan culture, where hand-painted wax patterns are coloured using specialist dyeing methods to create the finished product. This paper presents a study of vision-based feature extraction of Batik images considering colour, texture and shape features to develop a comprehensive feature descriptor of Batik motifs. Wax drawn patterns are identified from the digital images of Batik motifs to retrieve an outline of patterns demarcating the different coloured layers generated by multiple stages of dyeing. Motifs with repetitive patterns are identified using the Local Binary Pattern (LBP) as a texture feature vector. Both RGB and L*a*b* colour schemes are studied in the representation of Batik motifs. The colour description is presented using Mini Batch K-Means which out-performed the widely used K-Means clustering method. Hu Moment Invariants are used for shape feature extraction, and Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. A comprehensive feature descriptor is developed to represent Batik designs, which could be used to recommend similar designs based on the shape and texture features of query images presented by the user.
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    Feature Descriptor for Sri Lankan Batik Patterns Using Hu Moment Invariants and GLCM
    (IEEE, 2021-08-11) Senarathna, B. P. H. K. M. D; Rajakaruna, R. M. T. P
    Batik is a traditional craft of designing patterned fabrics which hold high artistic value in Sri Lankan culture, where hand-painted wax patterns are coloured using specialist dyeing methods to create the finished product. This paper presents a study of vision-based feature extraction of Batik images considering colour, texture and shape features to develop a comprehensive feature descriptor of Batik motifs. Wax drawn patterns are identified from the digital images of Batik motifs to retrieve an outline of patterns demarcating the different coloured layers generated by multiple stages of dyeing. Motifs with repetitive patterns are identified using the Local Binary Pattern (LBP) as a texture feature vector. Both RGB and L*a*b* colour schemes are studied in the representation of Batik motifs. The colour description is presented using Mini Batch K-Means which out-performed the widely used K-Means clustering method. Hu Moment Invariants are used for shape feature extraction, and Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. A comprehensive feature descriptor is developed to represent Batik designs, which could be used to recommend similar designs based on the shape and texture features of query images presented by the user.
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    Melanoma Skin Cancer Detection Using Image Processing and Machine Learning Techniques
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ahmed Thaajwer, M.A.; Ishanka, U.A.P.
    In humans, skin cancer is the most common and severe type of cancer. Melanoma is a deadly type of skin cancer. If it identifies early stages, it can be easily cured. The formal method for diagnosing melanoma detection is the biopsy method. This method can be a very painful one and a time-consuming process. This study gives a computer-aided detection system for the early identification of melanoma. In this study, image processing techniques and the Support vector machine (SVM) algorithms are used to introduce an efficient diagnosing system. The affected skin image is taken, and it sent under several pre-processing techniques for getting the enhanced image and smoothed image. Then the image is sent through the segmentation process using morphological and thresholding methods. Some essential texture, color and shape features of the skin images are extracted. Gray Level Co-occurrence Matrix (GLCM) methodology is used for extracting texture features. These extracted GLCM, color and shape features are given as input to the SVM classifier. It classifies the given image into malignant melanoma or benign melanoma. High accuracy of 83% is achieved when we combine and apply the shape, color and GLCM features to the classifier.