Spike learning based Privacy Preservation of Internet of Medical Things in Metaverse.

With the rising trend of digital technologies, such as augmented and virtual reality, Metaverse has gained a notable popularity. The applications that will eventually benefit from Metaverse is the telemedicine and e-health fields. However, the data and techniques used for realizing the medical side of Metaverse is vulnerable to data and class leakage attacks. Most of the existing studies focus on either of the problems through encryption techniques or addition of noise. In addition, the use of encryption techniques affects the overall performance of the medical services, which hinders its realization. In this regard, we propose Generative adversarial networks and spike learning based convolutional neural network (GASCNN) for medical images that is resilient to both the data and class leakage attacks. We first propose the GANs for generating synthetic medical images from residual networks feature maps. We then perform a transformation paradigm to convert ResNet to spike neural networks (SNN) and use spike learning technique to encrypt model weights by representing the spatial domain data into temporal axis, thus making it difficult to be reconstructed. We conduct extensive experiments on publicly available MRI dataset and show that the proposed work is resilient to various data and class leakage attacks in comparison to existing state-of-the-art works (1.75x increase in FID score) with the exception of slightly decreased performance (less than 3%) from its ResNet counterpart. while achieving 52x energy efficiency gain with respect to standard ResNet architecture.

New technology using crystalline lens autofluorescence for presbyopia and cataract grading.

PURPOSE: To investigate whether fundus autofluorescence (FAF) obtained using an ultra-wide field (UWF) fundus camera with an artificial opacity pattern can grade the degree of presbyopia and nuclear cataract. METHODS: Sixty eyes of 30 patients were enrolled in this prospective diagnostic study. The nuclear cataract (nuclear color/opalescence (NC/NO)) was graded according to the Lens Opacity Classification System III. The monocular near point of accommodation (NPA) was measured in eyes with NC3/NO3 or less. The mean gray value difference between the central 8 artificial opacity lesions and peripheral 8 artificial opacity lesions in the retinal AF was measured. The correlation between the mean gray value difference, NPA, and nuclear cataract grade was analyzed. RESULTS: The mean nuclear cataract grade of 60 eyes was 3.2 ± 1.6 and mean NPA of 37 eyes was 45.3 ± 16.1 cm. The mean gray value differences increased with increasing nuclear cataract grade (eyes with NC/NO grade 1, 53.3 ± 11.4; 2, 78.3 ± 13.6; 3, 95.2 ± 12.2; 4, 101.6 ± 11.9; 5, 109.0 ± 22.9; and 6, 121.1 ± 12.0; p < 0.001). The mean gray value difference was positively correlated with both the monocular NPA (R2 = 0.637; ß coefficient = 1.009; 95% CI, 0.748 to 1.271; p < 0.001) and nuclear cataract grade (R2 = 0.661; ß coefficient = 12.437; 95% CI, 10.097 to 14.778; p < 0.001). CONCLUSIONS: The FAF camera with an artificial opacity pattern attached can be used to effectively diagnose the degree of presbyopia and nuclear cataract.

Optic disc localization in fundus images through accumulated directional and radial blur analysis.

Optic disc localization, a key preprocessing step in the analysis of color fundus images for diagnoses of eye diseases and the localization of various anatomical structures, is particularly challenging when input retina images contain abnormalities. In such cases, the disc can be confused with other anatomical structures such as fovea, exudates, vessel tree extraction, and retinopathy-related lesions. Herein, we present a method for effective optic disc detection and localization based on color and blur analysis. In this method, the input color fundus image is converted to CIE L*a*b* color space to enhance optic disc appearance and contrast, and the accumulated directional blur and extended-maxima transform are then applied to precisely extract optic disc candidates. Subsequently, radial blur is applied to each candidate to obtain better profiles and thus distinguish the optic disc from other candidates. Finally, the full width at 80% maximum (FW80M) metric is used to select the optic disc. The performance of the proposed method is evaluated using well-studied data sets, and comparison of the obtained results with those of state-of-the-art techniques reveals the effectiveness of our method and shows that it can precisely locate the disc position not only in normal cases but also in the presence of exudates and abnormalities.

Development of an experimental model for ocular toxicity screening in Zebrafish.

BACKGROUND: To investigate the efficacy of a novel experimental model for exploring visual function using a contrast-optomotor response (C-OMR) assay made by applying the contrast sensitivity test to the OMR assay in zebrafish. METHODS: Zebrafish larvae were treated with 0 (control), 5, 10, or 15 µM gentamicin and digoxin for 24 h at four days post-fertilization (dpf). Zebrafish larvae were assessed using the C-OMR assay with graded contrast gray-white stripes at 5 dpf, and the results were expressed as the percentage of larvae that finished swimming for 30 s (n = 20 per each group). The same C-OMR assay was repeated four times using different larvae. RESULTS: The percentage of larvae that finished swimming within 30 s was significantly reduced in larvae treated with 5, 10, and 15 µM gentamicin and 10 and 15 µM digoxin as compared to the Control groups. The C-OMR assay could distinguish that the decrease in visual function was different depending on the concentration of gentamicin and digoxin (5, 10, and 15 µM), whereas the OMR test with one contrast gray-white stripe could not. CONCLUSIONS: The method of analyzing zebrafish OMR using graded contrast gray-white stripes is more sensitive than the OMR assay alone and may be more useful for assessing the drug toxicity and eye-related diseases to improve the understanding of drug-induced ocular side effects in the clinic.

Leveraging hybrid biomarkers in clinical endpoint prediction.

BACKGROUND: Clinical endpoint prediction remains challenging for health providers. Although predictors such as age, gender, and disease staging are of considerable predictive value, the accuracy often ranges between 60 and 80%. An accurate prognosis assessment is required for making effective clinical decisions. METHODS: We proposed an extended prognostic model based on clinical covariates with adjustment for additional variables that were radio-graphically induced, termed imaging biomarkers. Eight imaging biomarkers were introduced and investigated in a cohort of 68 non-small cell lung cancer subjects with tumor internal characteristic. The subjects comprised of 40 males and 28 females with mean age at 68.7 years. The imaging biomarkers used to quantify the solid component and non-solid component of a tumor. The extended model comprises of additional frameworks that correlate these markers to the survival ends through uni- and multi-variable analysis to determine the most informative predictors, before combining them with existing clinical predictors. Performance was compared between traditional and extended approaches using Receiver Operating Characteristic (ROC) curves, Area under the ROC curves (AUC), Kaplan-Meier (KM) curves, Cox Proportional Hazard, and log-rank tests (p-value). RESULTS: The proposed hybrid model exhibited an impressive boosting pattern over the traditional approach of prognostic modelling in the survival prediction (AUC ranging from 77 to 97%). Four developed imaging markers were found to be significant in distinguishing between subjects having more and less dense components: (P = 0.002-0.006). The correlation to survival analysis revealed that patients with denser composition of tumor (solid dominant) lived 1.6-2.2 years longer (mean survival) and 0.5-2.0 years longer (median survival), than those with less dense composition (non-solid dominant). CONCLUSION: The present study provides crucial evidence that there is an added value for incorporating additional image-based predictors while predicting clinical endpoints. Though the hypotheses were confirmed in a customized case study, we believe the proposed model is easily adapted to various clinical cases, such as predictions of complications, treatment response, and disease evolution.

Are shape morphologies associated with survival? A potential shape-based biomarker predicting survival in lung cancer.

PURPOSE: Imaging biomarkers (IBMs) are increasingly investigated as prognostic indicators. IBMs might be capable of assisting treatment selection by providing useful insights into tumor-specific factors in a non-invasive manner. METHODS: We investigated six three-dimensional shape-based IBMs: eccentricities between (I) intermediate-major axis (Eimaj), (II) intermediate-minor axis (Eimin), (III) major-minor axis (Emj-mn) and volumetric index of (I) sphericity (VioS), (II) flattening (VioF), (III) elongating (VioE). Additionally, we investigated previously established two-dimensional shape IBMs: eccentricity (E), index of sphericity (IoS), and minor-to-major axis length (Mn_Mj). IBMs were compared in terms of their predictive performance for 5-year overall survival in two independent cohorts of patients with lung cancer. Cohort 1 received surgical excision, while cohort 2 received radiation therapy alone or chemo-radiation therapy. Univariate and multivariate survival analyses were performed. Correlations with clinical parameters were evaluated using analysis of variance. IBM reproducibility was assessed using concordance correlation coefficients (CCCs). RESULTS: E was associated with reduced survival in cohort 1 (hazard ratio [HR]: 0.664). Eimin and VioF were associated with reduced survival in cohort 2 (HR 1.477 and 1.701). VioS was associated with reduced survival in cohorts 1 and 2 (HR 1.758 and 1.472). Spherical tumors correlated with shorter survival durations than did irregular tumors (median survival difference: 1.21 and 0.35 years in cohorts 1 and 2, respectively). VioS was a significant predictor of survival in multivariate analyses of both cohorts. All IBMs showed good reproducibility (CCC ranged between 0.86-0.98). CONCLUSIONS: In both investigated cohorts, VioS successfully linked shape morphology to patient survival.

Modeling global geometric spatial information for rotation invariant classification of satellite images.

The classification of high-resolution satellite images is an open research problem for computer vision research community. In last few decades, the Bag of Visual Word (BoVW) model has been used for the classification of satellite images. In BoVW model, an orderless histogram of visual words without any spatial information is used as image signature. The performance of BoVW model suffers due to this orderless nature and addition of spatial clues are reported beneficial for scene and geographical classification of images. Most of the image representations that can compute image spatial information as are not invariant to rotations. A rotation invariant image representation is considered as one of the main requirement for satellite image classification. This paper presents a novel approach that computes the spatial clues for the histograms of BoVW model that is robust to the image rotations. The spatial clues are calculated by computing the histograms of orthogonal vectors. This is achieved by calculating the magnitude of orthogonal vectors between Pairs of Identical Visual Words (PIVW) relative to the geometric center of an image. The comparative analysis is performed with recently proposed research to obtain the best spatial feature representation for the satellite imagery. We evaluated the proposed research for image classification using three standard image benchmarks of remote sensing. The results and comparisons conducted to evaluate this research show that the proposed approach performs better in terms of classification accuracy for a variety of datasets based on satellite images.

Optical brush: Imaging through permuted probes.

The combination of computational techniques and ultrafast imaging have enabled sensing through unconventional settings such as around corners, and through diffusive media. We exploit time of flight (ToF) measurements to enable a flexible interface for imaging through permuted set of fibers. The fibers are randomly distributed in the scene and are packed on the camera end, thus making a brush-like structure. The scene is illuminated by two off-axis optical pulses. Temporal signatures of fiber tips in the scene are used to localize each fiber. Finally, by combining the position and measured intensity of each fiber, the original input is reconstructed. Unlike conventional fiber bundles with packed set of fibers that are limited by a narrow field of view (FOV), lack of flexibility, and extended coaxial precalibration, the proposed optical brush is flexible and uses off-axis calibration method based on ToF. The enabled brush form can couple to other types of ToF imaging systems. This can impact probe-based applications such as, endoscopy, tomography, and industrial imaging and sensing.

Robust focus measure operator using adaptive log-polar mapping for three-dimensional shape recovery.

Shape from focus (SFF) is a passive optical technique that reconstructs object shape from a sequence of image taken at different focus levels. In SFF techniques, computing focus measurement for each pixel in the image sequence, through a focus measure operator, is the fundamental step. Commonly used focus measure operators compute focus quality in Cartesian space and suffer from erroneous focus quality and lack in robustness. Thus, they provide erroneous depth maps. In this paper, we introduce a new focus measure operator that computes focus quality in log-polar transform (LPT) Properties of LPT, such as biological inspiration, data selection, and edge invariance, enable computation of better focus quality in the presence of noise. Moreover, instead of using a fixed patch of the image, we suggest the use of an adaptive window. The focus quality is assessed by computing variation in LPT. The effectiveness of the proposed technique is evaluated by conducting experiments using image sequences of different simulated and real objects. The comparative analysis shows that the proposed method is robust and effective in the presence of various types of noise.

Robust depth estimation and image fusion based on optimal area selection.

Mostly, 3D cameras having depth sensing capabilities employ active depth estimation techniques, such as stereo, the triangulation method or time-of-flight. However, these methods are expensive. The cost can be reduced by applying optical passive methods, as they are inexpensive and efficient. In this paper, we suggest the use of one of the passive optical methods named shape from focus (SFF) for 3D cameras. In the proposed scheme, first, an adaptive window is computed through an iterative process using a criterion. Then, the window is divided into four regions. In the next step, the best focused area among the four regions is selected based on variation in the data. The effectiveness of the proposed scheme is validated using image sequences of synthetic and real objects. Comparative analysis based on statistical metrics correlation, mean square error (MSE), universal image quality index (UIQI) and structural similarity (SSIM) shows the effectiveness of the proposed scheme.