Non-Destructive Testing (NDT) is one of the inspection techniques used in industrial tool inspection for quality and safety control. It is performed mainly using X-ray Computed Tomography (CT) to scan the internal structure of the tools and detect the potential defects. In this paper, we propose a new toolbox called the CT-Based Integrity Monitoring System (CTIMS-Toolbox) for automated inspection of CT images and volumes. It contains three main modules: first, the database management module, which handles the database and reads/writes queries to retrieve or save the CT data; second, the pre-processing module for registration and background subtraction; third, the defect inspection module to detect all the potential defects (missing parts, damaged screws, etc.) based on a hybrid system composed of computer vision and deep learning techniques. This paper explores the different features of the CTIMS-Toolbox, exposes the performance of its modules, compares its features to some existing CT inspection toolboxes, and provides some examples of the obtained results.

We, hereby declare that this thesis is based on results we have found ourselves. Materials of work from researchers conducted by others are mentioned in references.

Image segmentation is one of the most important branches of image processing. But it comes with various challenges and problems to be solved. Researchers are always working on improving the accuracy, quality and performance of image segmentation techniques. As in modern days, deep learning being involved in almost all problem solving, it is being used in image segmentation too. In this paper, we discussed few image segmentation techniques developed using deep learning, some implementation of these techniques to applications. And lastly, we addressed some limitations, challenges and research scopes for future.