Purpose: Utilization of computer-aided diagnosis (CAD) on radiological ultrasound (US) imaging has increased tremendously. The prominent CAD applications are found in breast and thyroid cancer investigation. To make appropriate clinical recommendations, it is important to accurately segment the cancerous object called a lesion. Segmentation is a crucial step but undoubtedly a challenging problem due to various perturbations, e.g., speckle noise, intensity inhomogeneity, and low contrast.
Approach: We present a combinatorial framework for US image segmentation using a bilateral filter (BF) and hybrid region-edge-based active contour (AC) model. The BF is adopted to smooth images while preserving edges. Then the hybrid model of region and edge-based AC is applied along the scales in a global-to-local manner to capture the lesion areas. The framework was tested in segmenting 258 US images of breast and thyroid, which were validated by manual ground truths.
Results: The proposed framework is accessed quantitatively based on the overlapping values of the Dice coefficient, which reaches 90.05 ± 5.81 % . The evaluation with and without the BF shows that the enhancement procedure improves the framework well.
Conclusions: The high performance of the proposed method in our experimental results indicates its potential for practical implementations in CAD radiological US systems.
Remote eye trackers with consumer price have been used for various applications on flat computer screen. On the other hand, 3D gaze tracking in physical environment has been useful for visualizing gaze behavior, robots controller, and assistive technology. Instead of using affordable remote eye trackers, 3D gaze tracking in physical environment has been performed using corporate-level head mounted eye trackers, limiting its practical usage to niche user. In this research, we propose a novel method to estimate 3D gaze using consumer-level remote eye tracker. We implement geometric approach to obtain 3D point of gaze from binocular lines-of-sight. Experimental results show that the proposed method yielded low errors of 3.47±3.02 cm, 3.02±1.34 cm, and 2.57±1.85 cm in X, Y , and Z dimensions, respectively. The proposed approach may be used as a starting point for designing interaction method in 3D physical environment.
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