Studies on neutron diffraction and X-ray radiography for material inspection

Among the different probes to study the structures of the bio and structural materials, X-ray and neutron are widely used because of their distinctive usefulness in investigating different structures. X-ray radiography and neutron diffraction are two widely known non-destructive techniques for material inspection. Here we demonstrate the design of neutron diffractometer with low power source and analyze the digital image produced by the X-ray radiography instead of neutron diffraction because of the availability of the data. Neutron diffraction is a powerful tool for understanding the behavior of crystal structures and phase behaviors of materials. While neutron diffraction capabilities continue to explore new frontiers of materials science, such capabilities currently exist in limited places, which require high neutron flux. The study seeks to design a low-resolution neutron diffraction system that can be installed on low power reactors (e.g. 250 kW thermal power). The performance of the diffractometer is estimated using Monte-Carlo ray-tracing simulations with McStas with an application in material science. Both monochromatic and polychromatic configurations are considered in order to maximize the net diffracted neutron flux at the detectors with reasonable resolution. On the other hand, considering X-ray radiography as a structure inspecting technique, analysis of dental X-ray panorama is performed for the detection of oral lesions. A novel automatic computer-aided method to identify dental lesions from dental X-ray is presented. Morphological operations, intensity profile analysis, automated seed point selection, region growing, feature extraction and neural network application are carried out to perform the job. Results show that the performance of the proposed method surpasses existing automated methods utilizing dental X-rays --Abstract, page iii

Design An Intelligent System to Support Dental Cyst Detection Using Two Convolutional Neural Networks

The aim of this paper is to develop a methodology, through studies on Computer Vision techniques, for the automatic identification of dental cysts in panoramic radiography images, providing Dental professionals with an alternative aid in the interpretation of these images. In addition segmentation techniques are applied in the inner region of the jaws, seeking to separate the regions with a greater possibility of cyst. The objective of this work is to design an intelligent system that supports the diagnosis of Dental Cyst using convolutional neural networks in order to help detect Dental Cyst at an early stage. The research method applied in this study consists of model design, where built and trained two convolutional neural network architectures, supporting 80% of the dataset with a total of 775 images with four image categories, and proposal validation, where we work with the remaining 20% of the dataset. Our results show that the ResNet50 architecture achieved the best classification with an accuracy of 98%

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

Segmentation of the Oral and Facial Regions from Imaging Modalities with Reduced or No Ionizing Radiation

Ph.DDOCTOR OF PHILOSOPH

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis