Color image segmentation using adaptive color quantization and multiresolution texture characterization

An, Ning-Yu; Pun, Chi‐Man

doi:10.1007/s11760-012-0340-2

Cited by 30 publications

(15 citation statements)

References 13 publications

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“…Similar occurrences are also mentioned by related works [5], [10]. When extracting characteristics from images, information acquisition process is required which is not frequently taken by medical staff.…”

Section: Resultssupporting

confidence: 71%

Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms

Seixas

Barbon

Mantovani

2015

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

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Misleading diagnosis of skin diseases may result in complications during the healing process. Skin images provide an important contribution to medical staff on storing and exchanging information to try preventing misdiagnosis. For such, image segmentation process may benefit from use of machine learning techniques, increasing simplicity of procedure, reducing computational costs and improving the diagnosis. This paper presents a comparison among different paradigms of machine learning to validate the segmentation of medical images of lower members ulcers, this segmentation allows wound pattern recognition to determinate injury region aiming at reducing the subjectivity of human evaluation.

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Section: Resultssupporting

confidence: 71%

Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms

Seixas

Barbon

Mantovani

2015

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

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“…In [19] is presented an energy function that combines color and edge for the segmentation, as in [20] that also uses two techniques for region extraction using wavelets transform for texture and contourlet transform on boundaries. Or other segmentation is presented based on color and texture in [21].…”

Section: Related Workmentioning

confidence: 99%

Color energy as a seed descriptor for image segmentation with region growing algorithms on skin wound images

Seixas

Barbon

Siqueira

et al. 2014

2014 IEEE 16th International Conference on E-Health Networking, Applications and Services (Healthcom)

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This paper presents a seed finding method for region growing segmentation approach using color channel energy in image regions. Instead of using the RGB system separated for each pixel, the proposal uses the energy on each color channel to improve the range of the possible values, then creates a more specific seed to detail different regions. Region size used to calculate energy was adjusted to verify the proposed method. Images used were real wound photos, taken from patients undergoing treatment at the university hospital. Results showed that energy on regions presents enough information to segment, leading to a high percentage of matching with experts marks.

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“…41 The average performance of each method using the four quantitative measures, PRI, BDE, GCE, and VoI, is presented in Table 3. For the JSEG and the CTM approaches, the source codes provided at their web pages were downloaded and executed with the 300 BSD images.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…For the CSC method, the source code is not available; hence, the quantitative results presented in the original article are taken. It is important to highlight that the CSC evaluation, as mentioned in the original article, 41 has been carried out using only a subset of 100 images from the complete set of 300 images from the BSD. This table shows that the best results for the GCE and the BDE measures are achieved by the proposed RCT method.…”

Section: Performance Evaluationmentioning

confidence: 99%

Integration of color and texture cues in a rough set–based segmentation method

Lizarraga-Morales

Sánchez-Yáñez

Ayala-Ramírez

et al. 2014

J. Electron. Imaging

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Abstract. We propose the integration of color and texture cues as an improvement of a rough set-based segmentation approach, previously implemented using only color features. Whereas other methods ignore the information of neighboring pixels, the rough set-based approximations associate pixels locally. Additionally, our method takes into account pixel similarity in both color and texture features. Moreover, our approach does not require cluster initialization because the number of segments is determined automatically. The color cues correspond to the a and b channels of the CIELab color space. The texture features are computed using a standard deviation map. Experiments show that the synergistic integration of features in this framework results in better segmentation outcomes, in comparison with those obtained by other related and state-of-the-art methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Color image segmentation using adaptive color quantization and multiresolution texture characterization

Cited by 30 publications

References 13 publications

Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms

Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms

Color energy as a seed descriptor for image segmentation with region growing algorithms on skin wound images

Integration of color and texture cues in a rough set–based segmentation method

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