Nevin Manimala

J Imaging. 2021 Aug 20;7(8):155. doi: 10.3390/jimaging7080155.

ABSTRACT

Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3-10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference.

PMID:34460791 | DOI:10.3390/jimaging7080155

J Imaging. 2021 Jul 30;7(8):128. doi: 10.3390/jimaging7080128.

ABSTRACT

To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences in the private life of every involved person. State-of-the-art proliferates with solutions using deep neural networks to detect a fake multimedia content but unfortunately these algorithms appear to be neither generalizable nor explainable. However, traces left by Generative Adversarial Network (GAN) engines during the creation of the Deepfakes can be detected by analyzing ad-hoc frequencies. For this reason, in this paper we propose a new pipeline able to detect the so-called GAN Specific Frequencies (GSF) representing a unique fingerprint of the different generative architectures. By employing Discrete Cosine Transform (DCT), anomalous frequencies were detected. The β statistics inferred by the AC coefficients distribution have been the key to recognize GAN-engine generated data. Robustness tests were also carried out in order to demonstrate the effectiveness of the technique using different attacks on images such as JPEG Compression, mirroring, rotation, scaling, addition of random sized rectangles. Experiments demonstrated that the method is innovative, exceeds the state of the art and also give many insights in terms of explainability.

PMID:34460764 | DOI:10.3390/jimaging7080128

J Imaging. 2020 Sep 17;6(9):97. doi: 10.3390/jimaging6090097.

ABSTRACT

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study that uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17-0.19 and 0.20-0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels.

PMID:34460754 | DOI:10.3390/jimaging6090097

J Imaging. 2020 Aug 24;6(9):84. doi: 10.3390/jimaging6090084.

ABSTRACT

We present a numerical illumination model to calculate direct as well as diffuse or Hapke scattered radiation scenarios on arbitrary planetary surfaces. This includes small body surfaces such as main belt asteroids as well as e.g., the lunar surface. The model is based on the ray tracing method. This method is not restricted to spherical or ellipsoidal shapes but digital terrain data of arbitrary spatial resolution can be fed into the model. Solar radiation is the source of direct radiation, wavelength-dependent effects (e.g. albedo) can be accounted for. Mutual illumination of individual bodies in implemented (e.g. in binary or multiple systems) as well as self-illumination (e.g. crater floors by crater walls) by diffuse or Hapke radiation. The model is validated by statistical methods. A χ2 test is utilized to compare simulated images with DAWN images acquired during the survey phase at small body 4 Vesta and to successfully prove its validity.

PMID:34460741 | DOI:10.3390/jimaging6090084

J Imaging. 2021 Mar 12;7(3):53. doi: 10.3390/jimaging7030053.

ABSTRACT

Digital images represent the primary tool for diagnostics and documentation of the state of preservation of artifacts. Today the interpretive filters that allow one to characterize information and communicate it are extremely subjective. Our research goal is to study a quantitative analysis methodology to facilitate and semi-automate the recognition and polygonization of areas corresponding to the characteristics searched. To this end, several algorithms have been tested that allow for separating the characteristics and creating binary masks to be statistically analyzed and polygonized. Since our methodology aims to offer a conservator-restorer model to obtain useful graphic documentation in a short time that is usable for design and statistical purposes, this process has been implemented in a single Geographic Information Systems (GIS) application.

PMID:34460709 | DOI:10.3390/jimaging7030053

J Imaging. 2021 Mar 8;7(3):49. doi: 10.3390/jimaging7030049.

ABSTRACT

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.

PMID:34460705 | DOI:10.3390/jimaging7030049

J Imaging. 2020 Jul 30;6(8):75. doi: 10.3390/jimaging6080075.

ABSTRACT

The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.

PMID:34460690 | DOI:10.3390/jimaging6080075

J Imaging. 2021 Apr 28;7(5):81. doi: 10.3390/jimaging7050081.

ABSTRACT

The COVID-19 pandemic has been deemed a global health pandemic. The early detection of COVID-19 is key to combating its outbreak and could help bring this pandemic to an end. One of the biggest challenges in combating COVID-19 is accurate testing for the disease. Utilizing the power of Convolutional Neural Networks (CNNs) to detect COVID-19 from chest X-ray images can help radiologists compare and validate their results with an automated system. In this paper, we propose a carefully designed network, dubbed CORONA-Net, that can accurately detect COVID-19 from chest X-ray images. CORONA-Net is divided into two phases: (1) The reinitialization phase and (2) the classification phase. In the reinitialization phase, the network consists of encoder and decoder networks. The objective of this phase is to train and initialize the encoder and decoder networks by a distribution that comes out of medical images. In the classification phase, the decoder network is removed from CORONA-Net, and the encoder network acts as a backbone network to fine-tune the classification phase based on the learned weights from the reinitialization phase. Extensive experiments were performed on a publicly available dataset, COVIDx, and the results show that CORONA-Net significantly outperforms the current state-of-the-art networks with an overall accuracy of 95.84%.

PMID:34460677 | DOI:10.3390/jimaging7050081

J Imaging. 2020 Jul 2;6(7):60. doi: 10.3390/jimaging6070060.

ABSTRACT

Image fusion is a process that integrates similar types of images collected from heterogeneous sources into one image in which the information is more definite and certain. Hence, the resultant image is anticipated as more explanatory and enlightening both for human and machine perception. Different image combination methods have been presented to consolidate significant data from a collection of images into one image. As a result of its applications and advantages in variety of fields such as remote sensing, surveillance, and medical imaging, it is significant to comprehend image fusion algorithms and have a comparative study on them. This paper presents a review of the present state-of-the-art and well-known image fusion techniques. The performance of each algorithm is assessed qualitatively and quantitatively on two benchmark multi-focus image datasets. We also produce a multi-focus image fusion dataset by collecting the widely used test images in different studies. The quantitative evaluation of fusion results is performed using a set of image fusion quality assessment metrics. The performance is also evaluated using different statistical measures. Another contribution of this paper is the proposal of a multi-focus image fusion library, to the best of our knowledge, no such library exists so far. The library provides implementation of numerous state-of-the-art image fusion algorithms and is made available publicly at project website.

PMID:34460653 | DOI:10.3390/jimaging6070060

J Orthop Res. 2021 Aug 30. doi: 10.1002/jor.25176. Online ahead of print.

ABSTRACT

Injured tendons do not regain their native structure except at fetal or very young ages. Healing tendons often show mucoid degeneration involving accumulation of sulfated glycosaminoglycans (GAGs), but its etiology and molecular base have not been studied substantially. We hypothesized that quality and quantity of gene expression involving synthesis of proteoglycans having sulfated GAGs are altered in injured tendons and that a reduction in synthesis of sulfated GAGs improves structural and functional recovery of injured tendons. C57BL6/j mice were subjected to the Achilles tendon tenotomy surgery. The injured tendons accumulated sulfate proteoglycans as early as 1-week postsurgery and continued so by 4-week postsurgery. Transcriptome analysis revealed upregulation of a wide range of proteoglycan genes that have sulfated GAGs in the injured tendons 1 and 3 weeks postsurgery. Genes critical for enzymatic reaction of initiation and elongation of chondroitin sulfate GAG chains were also upregulated. After the surgery, mice were treated with the 2-deoxy-D-glucose (2DG) that inhibits conversion of glucose to glucose-6-phosphate, an initial step of glucose metabolism as an energy source and precursors of monosaccharides of GAGs. The 2DG treatment reduced accumulation of sulfated proteoglycans, improved collagen fiber alignment and reduced the cross-sectional area of the injured tendons. The modulus of the 2DG-treated groups were higher than that in the vehicle group, but not of statistical significance. Our findings suggest that mucoid degeneration in injured tendons may result from upregulated expression of genes involved synthesis of sulfate proteoglycans and can be inhibited by reduction of glucose utilization. This article is protected by copyright. All rights reserved.

PMID:34460123 | DOI:10.1002/jor.25176