High-frequency distortion detection in evaluating light field quality via axially symmetric dual-fan filtering.

The growing adoption of light field imaging in computational photography, autonomous driving, and immersive display systems has created an urgent need for accurate quality assessment. While Gabor-based methods can effectively analyze texture through multi-scale representations, their conventional fixed-scale implementations lack adaptability to varying distortion levels, resulting in compromised accuracy and efficiency. To address this limitation, we propose an adaptive-scale Gabor framework for light field image quality assessment (LFIQA) that dynamically adjusts feature extraction according to distortion severity.

A Bi-Directionally Fused Boundary Aware Network for Skin Lesion Segmentation.

It is quite challenging to visually identify skin lesions with irregular shapes, blurred boundaries and large scale variances. Convolutional Neural Network (CNN) extracts more local features with abundant spatial information, while Transformer has the powerful ability to capture more global information but with insufficient spatial details. To overcome the difficulties in discriminating small or blurred skin lesions, we propose a Bi-directionally Fused Boundary Aware Network (BiFBA-Net).

Smoke-Aware Global-Interactive Non-Local Network for Smoke Semantic Segmentation.

Compared with other objects, smoke semantic segmentation (SSS) is more difficult and challenging due to some special characteristics of smoke, such as non-rigid, translucency, variable mode and so on. To achieve accurate positioning of smoke in real complex scenes and promote the development of intelligent fire detection, we propose a Smoke-Aware Global-Interactive Non-local Network (SAGINN) for SSS, which harness the power of both convolution and transformer to capture local and global information simultaneously. Non-local is a powerful means for modeling long-range context dependencies, however, friendliness to single-scale low-resolution features limits its potential to produce high-quality representations.

A Gated Recurrent Network With Dual Classification Assistance for Smoke Semantic Segmentation.

Smoke has semi-transparency property leading to highly complicated mixture of background and smoke. Sparse or small smoke is visually inconspicuous, and its boundary is often ambiguous. These reasons result in a very challenging task of separating smoke from a single image.

Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic Semantics.

Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC.

Segmentation of breast ultrasound image with semantic classification of superpixels.

Breast cancer is a great threat to females. Ultrasound imaging has been applied extensively in diagnosis of breast cancer. Due to the poor image quality, segmentation of breast ultrasound (BUS) image remains a very challenging task.

A Wave-shaped Deep Neural Network for Smoke Density Estimation.

Smoke density estimation from a single image is a totally new but highly ill-posed problem. To solve the problem, we stack several convolutional encoder-decoder structures together to propose a wave-shaped neural network, termed W-Net. Stacking encoder-decoders directly increases the network depth, leading to the enlargement of receptive fields for encoding more semantic information.

A variational surface deformation and subdivision-based modeling framework for noisy and small n-furcated tube-like structures.

It is challenging to construct an accurate and smooth mesh for noisy and small n-furcated tube-like structures, such as arteries, veins, and pathological vessels, due to tiny vessel size, noise, n -furcations, and irregular shapes of pathological vessels. We propose a framework by dividing the modeling process into mesh construction and mesh refinement. In the first step, we focus on mesh topological correctness, and just create an initial rough mesh for the n-furcated tube-like structures.

Modeling n-furcated liver vessels from a 3-D segmented volume using hole-making and subdivision methods.

It is difficult to build an accurate and smooth liver vessel model due to the tiny size, noise, and n-furcations of vessels. To overcome these problems, we propose an n-furcation vessel tree modeling method. In this method, given a segmented volume and a point indicating the root of the vessels, centerlines and cross-sectional contours of the vessels are extracted and organized as a tree first.

[Fast extraction of iso-surface in surface rendering of medical images].

In this paper a new direct volume rendering method is presented for fast extraction of iso-surface by adopting the idea from the Shear-Warp algorithm. By creating the sorted volumetric data from the original volume data and specifying a value range of data which determines the part of the sorted volumetric data traversed, the amount of volume data traversed would be reduced obviously and the extraction operation of iso-surface would be very fast. In addition, we can adjust the value range to obtain the different rendering speed and image quality according to the purpose in application.