J. Imaging, Volume 6, Issue 2 (February 2020) – 3 articles

The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness. Full article

An impossible structure gives us the impression of looking at a three-dimensional object, even though this object cannot exist, since it possesses parts that are spatially non-connectable, and are characterized by misleading geometrical properties not instantly evident. Therefore, impossible artworks appeal to our intellect and challenge our perceptive capacities. We analyzed lithographs containing impossible structures (e.g., the Necker cube), created by the famous Dutch painter Maurits Cornelis Escher (1898–1972), and used one of them (The Belvedere, 1958) to unveil the artist’s hidden secrets by means of a discrete model of the human retina based on a non-uniform distribution of receptive fields. We demonstrated that the ability of Escher in composing his lithographs by connecting spatial coherent details into an impossible whole lies in drawing these incoherent fragments just outside the zone in which 3D coherence can be perceived during a single fixation pause. The main aspects of our paper from the point of view of image processing and image understanding are the following: (1) the peculiar and original digital filter to process the image, which simulates the human vision process, by producing a space-variant sampling of the image; (2) the software for the filter, which is homemade and created for our purposes. The filtered images resulting from the processing are used to understand impossible figures. As an example, we demonstrate how the impossible figures hidden in Escher’s paintings can be understood. Full article

Multichannel images, i.e., images of the same object or scene taken in different spectral bands or with different imaging modalities/settings, are common in many applications. For example, multispectral images contain several wavelength bands and hence, have richer information than color images. Multichannel magnetic resonance imaging and multichannel computed tomography images are common in medical imaging diagnostics, and multimodal images are also routinely used in art investigation. All the methods for grayscale images can be applied to multichannel images by processing each channel/band separately. However, it requires vast computational time, especially for the task of searching for overlapping patches similar to a given query patch. To address this problem, we propose a three-dimensional orthonormal tree-structured Haar transform (3D-OTSHT) targeting fast full search equivalent for three-dimensional block matching in multichannel images. The use of a three-dimensional integral image significantly saves time to obtain the 3D-OTSHT coefficients. We demonstrate superior performance of the proposed block matching. Full article