Manchester Metropolitan University's Research Repository

    Osteoporosis Classification Using Texture Features

    Riaz, Farhan, Nemati, Rehan, Ajmal, Hina, Hassan, Ali, Edifor, ernest ORCID logoORCID: https://orcid.org/0000-0001-9768-7360 and Nawaz, Raheel ORCID logoORCID: https://orcid.org/0000-0001-9588-0052 (2019) Osteoporosis Classification Using Texture Features. In: 32nd IEEE CBMS International Symposium on Computer-Based Medical Systems (IEEE CBMS'19), 05 June 2019 - 07 June 2019, Cordoba, Spain.

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    Assessment of osteoporotic disease from the radiograph image is a significant challenge. Texture characteristics when observed from the naked eye for the bone microarchitecture of the osteoporotic and healthy cases are visually very similar making it a challenging classification problem. To extract the discriminative patterns in all the orientations and scales simultaneously in this study we have proposed an approach that is based on a combination of multi resolution Gabor filters and 1D local binary pattern (1DLBP) features. Gabor filter are used due to their advantages in yielding a scale and orientation sensitive analysis whereas LBPs are useful for quantifying microstructural changes in the images. Our experiment show that the proposed method shows good classification results with an overall accuracy of about 72.71% and outperforms the other methods that have been considered in this paper.

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