Entropy-based automatic segmentation of bones in digital X-ray images

Authors:
Oishila Bandyopadhyay;Bhabatosh Chanda;Bhargab B. Bhattacharya
Affiliations:
Department of CSE, Camellia Institute of Technology, Kolkata, India;Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India;Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India
Venue:
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Year:
2011

Citing 6
Cited 0

Thresholding for edge detection using human psychovisual phenomena

Pattern Recognition Letters
Local entropy-based transition region extraction and thresholding

Pattern Recognition Letters
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
Interactive image segmentation by maximal similarity based region merging

Pattern Recognition
Gradient histogram: Thresholding in a region of interest for edge detection

Image and Vision Computing
Linear combinations of morphological operators: the midrange, pseudomedian, and LOCO filters

ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bone image segmentation is an integral component of orthopedic Xray image analysis that aims at extracting the bone structure from the muscles and tissues. Automatic segmentation of the bone part in a digital X-ray image is a challenging problem because of its low contrast with the surrounding flesh, which itself needs to be discriminated against the background. The presence of noise and spurious edges further complicates the segmentation. In this paper, we propose an efficient entropy-based segmentation technique that integrates several simple steps, which are fully automated. Experiments on several X-ray images reveal encouraging results as evident from a segmentation entropy quantitative assessment (SEQA) metric [Hao, et al. 2009].