Wyniki wyszukiwania - BazTech

1

Detection of Modic changes in MR images of spine using local binary patterns

Athertya Jiyo S., Saravana Kumar Gurunathan, Govindaraj Jayaraj

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

17--29

EN

Background and objective: With increase in prevalence of lower back pain, fast and reliable computer aided methods for clinical diagnosis associated with the same is needed for improving the healthcare reach. The magnetic resonance images exhibit a change in signal intensity on the vertebral body close to end plates, which are termed as Modic changes (MC), and are known to be clear indicators of lower back pain. The current work deals with computer aided methods for automating the classification of signal changes between normal and degenerate cases so as to aid physicians in precise and suitable diagnosis for the ailment. Methods: In order to detect Modic changes in vertebrae, initially the vertebrae are segmented from sagittal MR T1 and T2 imaged using a semi automatic cellular automata based segmentation. This is followed by textural feature extraction using Local Binary Patterns (LBP) and its variants. Various classifiers based on machine learning approaches using Random Forest, kNN, Bayes and SVM were evaluated for its classification performance. Since medical image dataset in general have bias towards healthy and diseased state, data augmentation techniques were also employed. Results: The implemented method is tested and validated over a dataset containing 100 patients. The proposed framework achieves an accuracy of 81% and 91.7% with and without augmentation of data respectively. A comparative study with the state of art methods reported in literature shows that the method proposed in better in terms of computational cost without any compromise on classification accuracy. Conclusion: A novel approach to identify MC in vertebrae by exploiting textural features is proposed. This shall assist radiologists in detecting abnormalities and in treatment planning.

2

Comparison of selected methods of characteristic point detection in satellite images

Kulawik J.

Przegląd Elektrotechniczny

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2018

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R. 94, nr 1

139--143

EN

The article presents two methods of detecting objects in images of the surface of the earth from the air. The search was performed using local characteristic features, i.e. key points. In the first method, the corner detection was supplied using the Harris & Stephens algorithm. The descriptors were built for detection key points by the FREAK algorithm. In the second method the blobs were provided by the SURF algorithm. The descriptors were built by the SURF algorithm. After the usage of the above methods, a comparison was made. The obtained results were shown on the example images.

PL

W artykule przedstawiono dwa przykłady detekcji obiektów w zdjęciach powierzchni ziemi z powietrza. Wyszukiwanie wykonano przy użyciu cech charakterystycznych. W pierwszym przykładzie dokonano detekcji narożników przy użyciu algorytmu Harris & Stephens. Następnie zbudowano deskryptory do znalezionych punktów kluczowych w oparciu o algorytm FREAK. W drugim przykładzie zastosowano metodę SURF do odnalezienia plamek i zbudowania ich deskryptorów. Po użyciu powyższych metod dokonano porównania. Uzyskane wyniki zaprezentowano na przykładowych zdjęciach.

3

The effect of patterns on image-based modelling of texture-less objects

Hafeez J., Jeon H.-J., Hamacher A., Kwon S.-C., Lee S.-H.

Metrology and Measurement Systems

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2018

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Vol. 25, nr 4

755--767

EN

The task of generating fast and accurate three-dimensional (3D) models of objects or scenes through a sequence of non-calibrated images is an active field of research. The recent development in algorithm optimization has resulted in many automatic solutions that can provide an accurate 3D model from texture-full objects. Structure-from-motion (SfM) is an image-based method that uses discriminative point-based feature identifier, such as SIFT, to locate feature points in the images. This method faces difficulties when presented with the objects made of homogenous or texture-less surfaces. To reconstruct such surfaces a well-known technique is to apply an artificial variety by covering the surface with a random texture pattern prior to the image capturing process. In this work, we designed three series of image patterns which are tested based on the contrast and density ratio which increases from the first to the last pattern within the same series. The performance of the patterns is evaluated by reconstructing the surface of a texture-less object and comparing it with the true data. Using the best-found patterns from the experiments, a 3D model of a Moai statue is reconstructed. The experimental results demonstrate that the density and structure of a pattern highly affects the quality of the reconstruction.

4

Analysis of operators for detection of corners set in automatic image matching

Zawieska D.

Archiwum Fotogrametrii, Kartografii i Teledetekcji

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2011

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Vol. 22

423--436

EN

Reconstruction of three dimensional models of objects from images has been a long lasting research topic in photogrammetry and computer vision. The demand for 3D models is continuously increasing in such fields as cultural heritage, computer graphics, robotics and many others. The number and types of features of a 3D model are highly dependent on the use of the models, and can be very variable in terms of accuracy and time for their creation. In last years, both computer vision and photogrammetric communities have approached the reconstruction problems by using different methods to solve the same tasks, such as camera calibration, orientation, object reconstruction and modelling. The terminology which is used for addressing the particular task in both disciplines is sometimes diverse. On the other hand, the integration of methods and algorithms coming from them can be used to improve both. The image based modelling of an object has been defined as a complete process that starts with image acquisition and ends with an interactive 3D virtual model. The photogrammetric approach to create 3D models involves the followings steps: image pre-processing, camera calibration, orientation of images network, image scanning for point detection, surface measurement and point triangulation, blunder detection and statistical filtering, mesh generation and texturing, visualization and analysis. Currently there is no single software package available that allows for each of those steps to be executed within the same environment. For high accuracy of 3D objects reconstruction operators are required as a preliminary step in the surface measurement process, to find the features that serve as suitable points when matching across multiple images. Operators are the algorithms which detect the features of interest in an image, such as corners, edges or regions. This paper reports on the first phase of research on the generation of high accuracy 3D model measurement and modelling, focusing upon the application of different operators for accurate feature point extraction. The implementation of those operators is discussed and performance of differen operators is analysed. The optimal operator for high accuracy close range object reconstruction is then highlighted. This research has facilitated a development of the feature extraction and image measurement process that will be central to the development of an automatic procedure for high accuracy point cloud generation in multi image networks where robust orientation and 3D point determination will facilitate surface measurement and modelling within a single software system.