Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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2017 - 201912020 - 20221

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Subject: search.filters.subject.Edge Detection

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    Segmentation and significance of herniation measurement using Lumbar Intervertebral Discs from the Axial View
    (IEEE, 2022-10-04) Siriwardhana, Y; Karunarathna, D; Ekanayake, I. U
    According to statistics, more than 60% of people suffer lower back pain at a certain time in their lives. Disc hernias are the most common cause of lower back pain, and the lumbar spine is responsible for more than 95% of all herniated discs. Generally, radiologists study the MRI during the clinical phase to detect a disc hernia. There could be several cases to evaluate, leaving the doctors to cogitate and envisage. Medical image segmentation aids in the diagnosis of spinal pathology, studying the anatomical structures, surgical procedures, and the evaluation of various treatments. However, manual segmentation of medical images necessitates a significant amount of time, effort, and discipline on the part of domain experts. This research study describes a framework that automates the segmentation of lumbar intervertebral discs using MRI images. Through this system, we can detect minor changes at the pixel level that are impossible to identify with the naked eye. We used convolutional neural networks with the UNet architecture to achieve the semantic segmentation process. The segmentations were evaluated using the Jacquard index and the dice coefficient.
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    Matlab based automated surface defect detection system for ceremic tiles using image processing
    (Faculty of Graduate Studies and Research, 2017-01-26) Samarawickrama, Y.C.; Wickramasinghe, C.D.
    In Ceramic tile industry the quality control process plays a major role to enhance quality standards. Still quality control of ceramic tile industry is done mostly by manually. Manual inspection is labor intensive, costly and less in efficiency. Further, the accuracy of the defect detection is lower due to harsh industrial environment and human errors. To overcome such drawbacks this project proposes an automated inspection system for ceramic tile industry based on image processing techniques. This system can detect color variations and defects such as corner damages, edge damages and middle cracks on the surface of the tile with high accuracy and efficiency. The tiles are compared with a good quality reference tile using image processing concepts using Matlab software. Based on this comparison the tile quality is classified. The system was checked with 110 real ceramic tiles consisting of defected tiles with cracks, corner damages and color variations. The results were outstanding with of 96.36% detection accuracy rate. The processing time for one tile was approximately 2 seconds. This outstanding achievement of results reflects that this automated system can effectively replace manual ceramic tile detection system with better accuracy and efficiency.