CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans

Segmentation of lung tissue in computed tomography (CT) images is a precursor to most pulmonary image analysis applications. Semantic segmentation methods using deep learning have exhibited top-tier performance in recent years, however designing accurate and robust segmentation models for lung tissue is challenging due to the variations in shape, size, and orientation. Additionally, medical image artifacts and noise can affect lung tissue segmentation and degrade the accuracy of downstream analysis. The practicality of current deep learning methods for lung tissue segmentation is limited as they require significant computational resources and may not be easily deployable in clinical settings. This paper presents a fully automatic method that identifies the lungs in three-dimensional (3D) pulmonary CT images using deep networks and transfer learning. We introduce (1) a novel 2.5-dimensional image representation from consecutive CT slices that succinctly represents volumetric information and (2) a U-Net architecture equipped with pre-trained InceptionV3 blocks to segment 3D CT scans while maintaining the number of learnable parameters as low as possible. Our method was quantitatively assessed using one public dataset, LUNA16, for training and testing and two public datasets, namely, VESSEL12 and CRPF, only for testing. Due to the low number of learnable parameters, our method achieved high generalizability to the unseen VESSEL12 and CRPF datasets while obtaining superior performance over Luna16 compared to existing methods (Dice coefficients of 99.7, 99.1, and 98.8 over LUNA16, VESSEL12, and CRPF datasets, respectively). We made our method publicly accessible via a graphical user interface at medvispy.ee.kntu.ac.ir

OBJECT-BASED IMAGE ENHANCEMENT TECHNIQUE FOR GRAY SCALE IMAGES

ABSTRACT Image enhancement technique plays vital role in improving the quality of the image. Enhancement technique basically enhances the foreground information and retains the background and improve the overall contrast of an image. In some case the background of an image hides the structural information of an image. This paper proposes an algorithm which enhances the foreground image and the background part separately and stretch the contrast of an image at inter-object level and intra-object level and then combines it to an enhanced image. The results are compared with various classical methods using image quality measures. KEYWORDS Image Enhancement, Morphological watershed segmentation, Object-based contrast enhancement, Interobject stretching, Intra-object stretching

OCR-directed evaluation of binarization techniques

The objective of this work is to study different binarization methods and to investigate their effect on the performance of OCR systems. Two sets of document images and four OCR systems were used to study several binarization algorithms. The simplest method that chooses the median value of the gray levels, i.e., 127 from 256 levels, as the global threshold value did not work well unless the scanner characteristic matched with the nature of a collection of documents by chance. The best-fixed method uses the global threshold value that minimizes the number of overall errors for a combination of an OCR system and a collection of documents. Both Otsu\u27s global algorithm and Niblack\u27s local algorithm performed, on the average, as well as the best-fixed method for the test data sets. The ideal global threshold method selects the best global threshold value for each combination of a page and an OCR system. Although the ideal method outperformed, on the average, Niblack\u27s method, Niblack\u27s method processed some images better than the ideal method

Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input

Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we present two novel approaches based on artificial intelligence that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative adversarial networks (GAN). Using large panoramic scanning electron microscopy images of dual-phase steels as a case study, we demonstrate how both methods effectively segment intricate microstructural details, including martensite, ferrite, and damage sites, for subsequent analysis. Either method shows substantial generalizability across a range of image sizes and conditions, including heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater step-by-step control and flexibility over the segmentation process. This study highlights the benefits of segmented microstructural data for various purposes, such as calculating phase fractions, modelling material behaviour through finite element simulation, and conducting geometrical analyses of damage sites and the local properties of their surrounding microstructure.Comment: 37 pages, 24 figure

Automated flaw detection method for X-ray images in nondestructive evaluation

Private, government and commercial sectors of the manufacturing world are plagued with imperfect materials, defective components, and aging assemblies that continuously infiltrate the products and services provided to the public. Increasing awareness of public safety and economic stability has caused the manufacturing world to search deeper for a solution to identify these mechanical weaknesses and thereby reduce their impact. The areas of digital image and signal processing have benefited greatly from the technological advances in computer hardware and software capabilities and the development of new processing methods resulting from extensive research in information theory, artificial intelligence, pattern recognition and related fields. These new processing methodologies and capabilities are laying a foundation of knowledge that empowers the industrial and academic community to boldly address this problem and begin designing and building better products and systems for tomorrow

Statistical Shape Modelling and Segmentation of the Respiratory Airway

The human respiratory airway consists of the upper (nasal cavity, pharynx) and the lower (trachea, bronchi) respiratory tracts. Accurate segmentation of these two airway tracts can lead to better diagnosis and interpretation of airway-specific diseases, and lead to improvement in the localization of abnormal metabolic or pathological sites found within and/or surrounding the respiratory regions. Due to the complexity and the variability displayed in the anatomical structure of the upper respiratory airway along with the challenges in distinguishing the nasal cavity from non-respiratory regions such as the paranasal sinuses, it is difficult for existing algorithms to accurately segment the upper airway without manual intervention. This thesis presents an implicit non-parametric framework for constructing a statistical shape model (SSM) of the upper and lower respiratory tract, capable of distinct shape generation and be adapted for segmentation. An SSM of the nasal cavity was successfully constructed using 50 nasal CT scans. The performance of the SSM was evaluated for compactness, specificity and generality. An averaged distance error of 1.47 mm was measured for the generality assessment. The constructed SSM was further adapted with a modified locally constrained random walk algorithm to segment the nasal cavity. The proposed algorithm was evaluated on 30 CT images and outperformed comparative state-of-the-art and conventional algorithms. For the lower airway, a separate algorithm was proposed to automatically segment the trachea and bronchi, and was designed to tolerate the image characteristics inherent in low-contrast CT images. The algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance (87.1±2.8 DSC and distance error of 0.37±0.08 mm) in segmentation results against comparative state-of-the-art algorithms