Adaptive segmentation and mask-specific Sobolev inpainting of specular highlights for endoscopic images.

Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance. In this paper, we propose a segmentation method of the specular regions based on an automatic color-adaptive threshold and a gradient-based edge detector. The segmented regions are then recovered using a robust mask-specific Sobolev inpainting approach. Experimental results demonstrate the precision and efficiency of the proposed method. In contrast to the existing approaches, the proposed solution does not require manual threshold selection or complex computations to achieve accurate results. Moreover, our method has a real-time performance and can be generalized to various applications.

Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery.

The lack of force feedback is considered one of the major limitations in Robot Assisted Minimally Invasive Surgeries. Since add-on sensors are not a practical solution for clinical environments, in this paper we present a force estimation approach that starts with the reconstruction of a 3D deformation structure of the tissue surface by minimizing an energy functional. A Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) based architecture is then presented to accurately estimate the applied forces. According to the results, our solution offers long-term stability and shows a significant percentage of accuracy improvement, ranging from about 54% to 78%, over existing approaches.

Automatic and robust single-camera specular highlight removal in cardiac images.

In computer-assisted beating heart surgeries, accurate tracking of the heart's motion is of huge importance and there is a continuous need to eliminate any source of error that might disturb the tracking process. One source of error is the specular reflection that appears on the glossy surface of the heart. In this paper, we propose a robust solution for the detection and removal of specular highlights. A hybrid color attributes and wavelet based edge projection approach is applied to accurately identify the affected regions. These regions are then recovered using a dynamic search-based inpainting with adaptive windowing. Experimental results demonstrate the precision and efficiency of the proposed method. Moreover, it has a real-time performance and can be generalized to various other applications.

Unconstrained ℓ1-regularized minimization with interpolated transformations for heart motion compensation.

Motion compensation constitutes a challenging issue in minimally invasive beating heart surgery. Since the zone to be repaired has a dynamic behaviour, precision and surgeon's dexterity decrease. In order to solve this problem, various proposals have been presented using ℓ2-norm. However, as they present some limitations in terms of robustness and efficiency, motion compensation is still considered an open problem. In this work, a solution based on the class of ℓ1 Regularized Optimization is proposed. It has been selected due to its mathematical properties and practical benefits. In particular, deformation is characterized by cubic B-splines since they offer an excellent balance between computational cost and accuracy. Moreover, due to the non-differentiability of the functional, the logarithmic barrier function is used for generating a standard optimization problem. Results have provided a very good tradeoff between accuracy and efficiency, indicating the potential of the proposed approach and proving its stability even under complex deformations.

State of the art survey on MRI brain tumor segmentation.

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.