3D volume localization using miniatures

The prediction of the position of a given volume sample in a full body atlas, also known as a volume localization, is a part of an initial stage of image retrieval in most of the dedicated CAD systems. In this paper we present two methods for volume localization, namely histogram matching and classifier regression. Since the histogram matching method ignores the spatial orientation, it is used when the orientation of the volume cubes are not the same. On the other hand the classifier regression is much faster and can be used as a quick estimation and as a tool to reduce the scope of the initial problem. Both presented methods were tested on a dataset with 3962 volumes of a human body atlas. The accuracy and the speed of execution was compared and is presented in this pap

3D volume localization using miniatures

The prediction of the position of a given volume sample in a full body atlas, also known as a volume localization, is a part of an initial stage of image retrieval in most of the dedicated CAD systems. In this paper we present two methods for volume localization, namely histogram matching and classifier regression. Since the histogram matching method ignores the spatial orientation, it is used when the orientation of the volume cubes are not the same. On the other hand the classifier regression is much faster and can be used as a quick estimation and as a tool to reduce the scope of the initial problem. Both presented methods were tested on a dataset with 3962 volumes of a human body atlas. The accuracy and the speed of execution was compared and is presented in this pap

LBP and irregular graph pyramids

In this paper, a new codification of Local Binary Patterns (LBP) is given using graph pyramids. The LBP code characterizes the topological category (local max, min, slope, saddle) of the gray level landscape around the center region. Given a 2D grayscale image I, our goal is to obtain a simplified image which can be seen as “minimal” representation in terms of topological characterization of I. For this, a method is developed based on merging regions and Minimum Contrast Algorithm

Evaluation of color differences in natural scene color images

Since there is a wide range of applications requiring image color difference (CD) assessment (e.g. color quantization, color mapping), a number of CD measures for images have been proposed. However, the performance evaluation of such measures often suffers from the following major flaws: (1) test images contain primarily spatial- (e.g. blur) rather than color-specific distortions (e.g. quantization noise), (2) there are too few test images (lack of variability in color content), and (3) test images are not publicly available (difficult to reproduce and compare). Accordingly, the performance of CD measures reported in the state-of-the-art is ambiguous and therefore inconclusive to be used for any specific color-related application. In this work, we review a total of twenty four state-of-the-art CD measures. Then, based on the findings of our review, we propose a novel method to compute CDs in natural scene color images. We have tested our measure as well as the state-of-the-art measures on three color related distortions from a publicly available database (mean shift, change in color saturation and quantization noise). Our experimental results show that the correlation between the subjective scores and the proposed measure exceeds 85% which is better than the other twenty four CD measures tested in this work (for illustration the best performing state-of-the-art CD measures achieve correlations with humans lower than 80%)

Effective recognition of facial micro-expressions with video motion magnification

Facial expression recognition has been intensively studied for decades, notably by the psychology community and more recently the pattern recognition community. What is more challenging, and the subject of more recent research, is the problem of recognizing subtle emotions exhibited by so-called micro-expressions. Recognizing a micro-expression is substantially more challenging than conventional expression recognition because these micro-expressions are only temporally exhibited in a fraction of a second and involve minute spatial changes. Until now, work in this field is at a nascent stage, with only a few existing micro-expression databases and methods. In this article, we propose a new micro-expression recognition approach based on the Eulerian motion magnification technique, which could reveal the hidden information and accentuate the subtle changes in micro-expression motion. Validation of our proposal was done on the recently proposed CASME II dataset in comparison with baseline and state-of-the-art methods. We achieve a good recognition accuracy of up to 75.30% by using leave-one-out cross validation evaluation protocol. Extensive experiments on various factors at play further demonstrate the effectiveness of our proposed approach

Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Information retrieval algorithms have changed the way we manage and use various data sources, such as images, music or multimedia collections. First, free text information of documents from varying sources became accessible in addition to structured data in databases, initially for exact search and then for more probabilistic models. Novel approaches enable content-based visual search of images using computerized image analysis making visual image content searchable without requiring high quality manual annotations. Other multimedia data followed such as video and music retrieval, sometimes based on techniques such as extracting objects and classifying genre. 3D (surface) objects and solid textures have also been produced in quickly increasing quantities, for example in medical tomographic imaging. For these two types of 3D information sources, systems have become available to characterize the objects or textures and search for similar visual content in large databases. With 3D moving sequences (i.e., 4D), in particular medical imaging, even higher-dimensional data have become available for analysis and retrieval and currently present many multimedia retrieval challenges. This article systematically reviews current techniques in various fields of 3D and 4D visual information retrieval and analyses the currently dominating application areas. The employed techniques are analysed and regrouped to highlight similarities and complementarities among them in order to guide the choice of optimal approaches for new 3D and 4D retrieval problems. Opportunities for future applications conclude the article. 3D or higher-dimensional visual information retrieval is expected to grow quickly in the coming years and in this respect this article can serve as a basis for designing new applications

A framework for cardio-pulmonary resuscitation (CPR) scene retrieval from medical simulation videos based on object and activity detection.

In this thesis, we propose a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical students, where an emergency patient condition is simulated on human-like mannequins and the students act upon. These simulation sessions are recorded by the physician, for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. The proposed framework for CPR scene retrieval, would eliminate the conventional approach of using shot detection and frame segmentation techniques. Firstly, our work explores the application of Histogram of Oriented Gradients in three dimensions (HOG3D) to retrieve the scenes containing CPR activity. Secondly, we investigate the use of Local Binary Patterns in Three Orthogonal Planes (LBPTOP), which is the three dimensional extension of the popular Local Binary Patterns. This technique is a robust feature that can detect specific activities from scenes containing multiple actors and activities. Thirdly, we propose an improvement to the above mentioned methods by a combination of HOG3D and LBP-TOP. We use decision level fusion techniques to combine the features. We prove experimentally that the proposed techniques and their combination out-perform the existing system for CPR scene retrieval. Finally, we devise a method to detect and retrieve the scenes containing the breathing bag activity, from the medical simulation videos. The proposed framework is tested and validated using eight medical simulation videos and the results are presented

Magneettiresonanssikuvien tekstuurianalyysisovelluksen kehittäminen MATLAB-ympäristössä

This thesis was based on the need to develop a generic software application frame for texture analysis of magnetic resonance (MR) images. In collaboration with the research group at the department of Medical Imaging Centre and Hospital Pharmacy (MICHP) at Tampere University Hospital (TAUH) the goal was to improve the user experience and work flow as well as implement a completely new user interface and key functionalities. The platform was required to be complex enough to manage with image processing algorithms and to provide high level and easily modifiable software architecture. The research group having years of experience with an open-source texture analysis oriented MaZda software the focus of this thesis was to analyse and solve the restrictions based on the observations from using MaZda. MATLAB was chosen as the programming platform due the high-level syntax with powerful built-in properties e.g. Image Processing Toolbox (IPT) that would allow proficient support for computationally demanding processes. Another advantage with MATLAB was the interface support for languages like Fortran, C and C++. MATLAB being commercial software platform, it was acknowledged that achieving a standalone end product would not be possible. Computational performance was also omitted for the purpose this thesis not only due to MATLAB’s limitations but also to keep the scale contained. The improvement suggestions provided by the research group were considered as a rough specification for the software to be implemented. These requirements included extensibility in terms of texture analysis algorithms and simplified user interface to improve the work flow. Selecting MATLAB as the programming environment extended the group of people capable of contributing to the tool in the future. Implementing the frame from the beginning allowed the texture analysis parameters and features to be fully configurable instead of static. The modular visual structure of the software allowed the user to switch between image sets more easily. Removing the region of interest (ROI) limitation ensured that same image set could be utilized more efficiently. The implemented MATLAB application provides a basic frame for more convenient medical image processing flow for texture analysis of MR images but further testing and development is required to complement the tool