Wyniki wyszukiwania - BazTech

1

Finger joint synovitis detection in ultrasound images

Radlak K., Frackiewicz M., Palus H., Smolka B.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2018

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Vol. 66, nr 2

235--245

EN

Ultrasonography has proved its usefulness in the evaluation of joint inflammations caused by rheumatoid arthritis. The illness severity is scored by human examiners based on their experience, but some discrepancies in the final diagnosis and treatment frequently occur. Therefore, the main aim of this work is the elaboration of an automatic method of the localization of finger joint inflammation level in ultrasound images. In this paper we propose a novel, fully automated framework for synovitis region segmentation. In our approach we compare several bones and joint localization methods based on the seeded region growing technique, which is combined with different speckle noise filtering algorithms. This technique extracts a region from the image using some predefined criteria of similarity between initially selected point and the pixels in its neighborhood. The seed point is localized automatically as the darkest patch within a small region between two detected finger bones close to the joint. The region affected by synovitis is found using the adopted criterion of homogeneity based on a patch to patch similarity measure. The obtained results exhibit a satisfying accuracy in comparison with the annotations prepared by an expert and the results delivered by semi-automatic methods that require manual bones delineation.

2

New Binary Hausdorff Symmetry measure based seeded region growing for retinal vessel segmentation

Panda R., Puhan N. B., Panda G.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

119--129

EN

Automated retinal vessel segmentation plays an important role in computer-aided diagnosis of serious diseases such as glaucoma and diabetic retinopathy. This paper contributes, (1) new Binary Hausdorff Symmetry (BHS) measure based automatic seed selection, and (2) new edge distance seeded region growing (EDSRG) algorithm for retinal vessel segmentation. The proposed BHS measure directly provides a binary symmetry decision at each pixel without the computation of continuous symmetry map and image thresholding. In a multiscale mask, the BHS measure is computed using the distance sets of opposite direction angle bins with sub-pixel resolution. The computation of the BHS measure from the Hausdorff distance sets involves point set matching based geometrical interpretation of symmetry. Then, we design a new edge distance seeded region growing (EDSRG) algorithm with the acquired seeds. The performance evaluation in terms of sensitivity, specificity and accuracy is done on the publicly available DRIVE, STARE and HRF databases. The proposed method is found to achieve state-of-the-art vessel segmentation accuracy in three retinal databases; DRIVE- sensitivity (0.7337), specificity (0.9752), accuracy (0.9539); STARE-sensitivity (0.8403), specificity (0.9547), accuracy (0.9424); and HRF-sensitivity (0.8159), specificity (0.9525), accuracy (0.9420).

3

Segmentation based on homomorphic filtering and improved seeded region growing for mobile robots tracking in image sequences

Kasinski A. J., Hamdy A. M.

Machine Graphics and Vision

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2001

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Vol. 10, No. 4

447-466

EN

In this paper, we present a method for extracting of mobile robots in a sequence of noisy frames, assuming a complex background composed of textured floor, illuminated unvenly. A homomorphic filter if used, as a preprocessor, to enhance the acquired frames by eliminating the illumination component and emphasizing the reflectance component of the image function. To speed up preprocessing of each frame, filtering is only applied to the pixels belonging to the regions of interest (ROI). In all the tested cases, homomorphic--filtering led to better results than those obtained without preprocessing. The segmentation process has been based on seeded region growing procedure for reconstructing the shape of the mobil robot. We proposed automatic seed points selection in the binarized difference image, and use an adaptive threshold. This use eliminates or at least considerably reduces false negative detections, and reduces sensitivity of aggregation results to the selected seed points as compared to the classical seeded region growing procedure. Additionally, by imposing a condition of strong connectivityu bettween a seed point and its neighborhood, aggregation of undesired pixels efficiently eliminates false positive detections. Implementation of segmentation and tracking can be run in real time. High tracking accuracy has been obtained through out all the frames in a test sequence.