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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

2019

Vol. 39, no. 1

17--29

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.

Comparison of selected methods of characteristic point detection in satellite images

Kulawik J.

Przegląd Elektrotechniczny

2018

R. 94, nr 1

139--143

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.

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.