GSNet: a multi-class 3D attention-based hybrid glioma segmentation network.

In modern neuro-oncology, computer-aided biomedical image retrieval (CBIR) tools have recently gained significant popularity due to their quick and easy usage and high-performance capability. However, designing such an automated tool remains challenging because of the lack of balanced resources and inconsistent spatial texture. Like in many other fields of diagnosis, brain tumor (glioma) extraction has posed a challenge to the research community. In this article, we proposed a fully developed robust segmentation network called GSNet for the purpose of glioma segmentation. Unlike conventional 2-dimensional structures, GSNet directly deals with 3-dimensional (3D) data while utilizing attention-based skip links. The network is trained and validated using the BraTS 2020 dataset and further trained with BraTS 2019 and BraTS 2018 datasets for comparison. While utilizing the BraTS 2020 dataset, our 3D network achieved an overall dice similarity coefficient of 0.9239, 0.9103, and 0.8139, respectively for whole tumor, tumor core, and enhancing tumor classes. Our model produces significantly high scores across all occasions and is capable of dealing with newer data, despite training with imbalanced datasets. In comparison to other articles, our model outperforms some of the state-of-the-art scores designating it to be suitable as a reliable CBIR tool for necessary medical usage.

Correction of geometrically distorted underwater images using shift map analysis.

In underwater imaging, water waves cause severe geometric distortions and blurring of the acquired short-exposure images. Corrections for these distortions have been tackled reasonably well by previous efforts but still need improvement in the estimation of pixel shift maps to increase restoration accuracy. This paper presents a new algorithm that efficiently estimates the shift maps from geometrically distorted video sequences and uses those maps to restore the sequences. A nonrigid image registration method is employed to estimate the shift maps of the distorted frames against a reference frame. The sharpest frame of the sequence, determined using a sharpness metric, is chosen as the reference frame. A k-means clustering technique is employed to discard too-blurry frames that could result in inaccuracy in the shift maps' estimation. The estimated pixel shift maps are processed to generate the accurate shift map that is used to dewarp the input frames into their nondistorted forms. The proposed method is applied on several synthetic and real-world video sequences, and the obtained results exhibit significant improvements over the state-of-the-art methods.

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images.

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

Geometric correction of atmospheric turbulence-degraded video containing moving objects.

Long-distance surveillance is a challenging task because of atmospheric turbulence that causes time-varying image shifts and blurs in images. These distortions become more significant as the imaging distance increases. This paper presents a new method for compensating image shifting in a video sequence while keeping real moving objects in the video unharmed. In this approach, firstly, a highly accurate and fast optical flow technique is applied to estimate the motion vector maps of the input frames and a centroid algorithm is employed to generate a geometrically correct frame in which there is no moving object. The second step involves applying an algorithm for detecting real moving objects in the video sequence and then restoring it with those objects unaffected. The performance of the proposed method is verified by comparing it with that of a state-of-the-art approach. Simulation experiments using both synthetic and real-life surveillance videos demonstrate that this method significantly improves the accuracy of image restoration while preserving moving objects.

Model-free prediction of atmospheric warp based on artificial neural network.

This paper presents the application of artificial neural network for predicting the warping of images of remote objects or scenes ahead of time. The algorithm is based on estimating the pattern of warping of previously captured short-exposure frames through a generalized regression neural network (GRNN) and then predicting the warping of the upcoming frame. A high-accuracy optical flow technique is employed to estimate the dense motion fields of the captured frames, which are considered as training data for the GRNN. The proposed approach is independent of the pixel-oscillatory model unlike the state-of-the-art Kalman filter (KF) approach. Simulation experiments on synthetic and real-world turbulence degraded videos show that the proposed GRNN-based approach performs better than the KF approach in atmospheric warp prediction.

Simple and efficient approach for restoration of non-uniformly warped images.

A high accuracy image dewarping method is proposed to restore images from non-uniformly warped video sequences degraded by atmospheric turbulence. This approach contains three major steps. First, a non-rigid image registration technique is employed to register all the frames in the sequence to a reference frame and estimate the motion fields. Second, an iterative First Register Then Average And Subtract (iFRTAAS) method is applied to correct the geometric deformations of the warped frames. The third step involves applying a non-local means filter for the compensation of noise and to improve the signal-to-noise ratio (SNR) of the restored reference frame. Simulations are carried out by applying the method to synthetic and real-life turbulence degraded videos and by determining various quality metrics. A performance comparison is presented between the proposed method and two earlier methods, which verifies that the proposed method provides significant improvement on the image restoration accuracy.

First record of sexual size dimorphism in fossil Strombidae (Mollusca, Gastropoda) from the Miocene of Kutch, western India and its evolutionary implications.

Persististrombus deperditus (Sowerby) from the Lower Miocene of Kutch, Gujarat, western India is represented by two size classes in our collection. Statistical analyses discriminate the size morphs. Large size variations generally result from either (1) sexual differences or (2) ecophenotypic causes. All the living species of the family Strombidae, wherever examined, are characterized by sexual size dimorphism (SSD). Persististrombus deperditus shares all the characters of SSD in these recent species. Size variations due to difference in ecological factors generally occur in allopatric populations. Similar variations are known to characterize sympatric sub-populations of molluscs living only in the intertidal zone, where upper and lower shorefaces differ significantly in physico-chemical and biological properties. Persististrombus deperditus comes from a stable shelf setting that received less siliciclastic input in response to transgression. Hence, its size dimorphism is considered to have sexual origin. This is the first report of SSD in a fossil strombid gastropod. It is argued that fecundity selection was the primary driving force behind the evolution of SSD in this gonochoristic gastropod species. Hence, the larger morph is the female.