DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images

Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License

A Multi-scale Bilateral Structure Tensor Based Corner Detector

9th Asian Conference on Computer Vision, ACCV 2009, Xi'an, 23-27 September 2009In this paper, a novel multi-scale nonlinear structure tensor based corner detection algorithm is proposed to improve effectively the classical Harris corner detector. By considering both the spatial and gradient distances of neighboring pixels, a nonlinear bilateral structure tensor is constructed to examine the image local pattern. It can be seen that the linear structure tensor used in the original Harris corner detector is a special case of the proposed bilateral one by considering only the spatial distance. Moreover, a multi-scale filtering scheme is developed to tell the trivial structures from true corners based on their different characteristics in multiple scales. The comparison between the proposed approach and four representative and state-of-the-art corner detectors shows that our method has much better performance in terms of both detection rate and localization accuracy.Department of ComputingRefereed conference pape

SCHLIEREN SEQUENCE ANALYSIS USING COMPUTER VISION

Computer vision-based methods are proposed for extraction and measurement of flow structures of interest in schlieren video. As schlieren data has increased with faster frame rates, we are faced with thousands of images to analyze. This presents an opportunity to study global flow structures over time that may not be evident from surface measurements. A degree of automation is desirable to extract flow structures and features to give information on their behavior through the sequence. Using an interdisciplinary approach, the analysis of large schlieren data is recast as a computer vision problem. The double-cone schlieren sequence is used as a testbed for the methodology; it is unique in that it contains 5,000 images, complex phenomena, and is feature rich. Oblique structures such as shock waves and shear layers are common in schlieren images. A vision-based methodology is used to provide an estimate of oblique structure angles through the unsteady sequence. The methodology has been applied to a complex flowfield with multiple shocks. A converged detection success rate between 94% and 97% for these structures is obtained. The modified curvature scale space is used to define features at salient points on shock contours. A challenge in developing methods for feature extraction in schlieren images is the reconciliation of existing techniques with features of interest to an aerodynamicist. Domain-specific knowledge of physics must therefore be incorporated into the definition and detec- tion phases. Known location and physically possible structure representations form a knowledge base that provides a unique feature definition and extraction. Model tip location and the motion of a shock intersection across several thousand frames are identified, localized, and tracked. Images are parsed into physically meaningful labels using segmentation. Using this representation, it is shown that in the double-cone flowfield, the dominant unsteady motion is associated with large scale random events within the aft-cone bow shock. Small scale organized motion is associated with the shock-separated flow on the fore-cone surface. We show that computer vision is a natural and useful extension to the evaluation of schlieren data, and that segmentation has the potential to permit new large scale measurements of flow motion

Bone fracture detection through X-ray using Edge detection Algorithms

Human beings are highly prone to bone fractures, to a great extent as an outcome of accidents or other factors such as bone cancer. Manual fracture detection takes a lengthy time and comes with a considerable chance of error. As a result, establishing a computer-based method to reduce fracture bone diagnosis time and risk of error is critical. The most common method for segmenting images based on sharp changes in intensity is edge detection. Sobel, Robert, Canny, Prewitt, and LoG (Laplacian of Gaussian) are some of the edge detection approaches that are examined for the study of bone fracture detection. The focal point of this paper is an endeavor to study, analyze and compare the Sobel, Canny, and Prewitt Techniques for detecting edges and identifying the fracture

Region of Interest Generation for Pedestrian Detection using Stereo Vision

Pedestrian detection is an active research area in the field of computer vision. The sliding window paradigm is usually followed to extract all possible detector windows, however, it is very time consuming. Subsequently, stereo vision using a pair of camera is preferred to reduce the search space that includes the depth information. Disparity map generation using feature correspondence is an integral part and a prior task to depth estimation. In our work, we apply the ORB features to fasten the feature correspondence process. Once the ROI generation phase is over, the extracted detector window is represented by low level histogram of oriented gradient (HOG) features. Subsequently, Linear Support Vector Machine (SVM) is applied to classify them as either pedestrian or non-pedestrian. The experimental results reveal that ORB driven depth estimation is at least seven times faster than the SURF descriptor and ten times faster than the SIFT descriptor

Addressing corner detection issues for machine vision based UAV aerial refueling

The need for developing autonomous aerial refueling capabilities for an Unmanned Aerial Vehicle (UAV) has risen out of the growing importance of UAVs in military and non-military applications. The AAR capabilities would improve the range and the loiter time capabilities of UAVs. A number of AAR techniques have been proposed, based on GPS based measurements and Machine Vision based measurements. The GPS based measurements suffer from distorted data in the wake of the tanker. The MV based techniques proposed the use of optical markers which---when detected---were used to determine relative orientation and position of the tanker and the UAV. The drawback of the MV based techniques is the assumption that all the optical markers are always visible and functional. This research effort proposes an alternative approach where the pose estimation does not depend on optical markers but on Feature Extraction methods. The thesis describes the results of the analysis of specific \u27corner detection\u27 algorithms within a Machine Vision---based approach for the problem of Aerial Refueling for Unmanned Aerial Vehicles. Specifically, the performances of the SUSAN and the Harris corner detection algorithms have been compared. Special emphasis was placed on evaluating their accuracy, the required computational effort, and the robustness of both methods to different sources of noise. Closed loop simulations were performed using a detailed SimulinkRTM -based simulation environment to reproduce docking maneuvers, using the US Air Force refueling boom