Rough and accurate segmentation of natural images using fuzzy region-growing algorithm

Maeda, Junji; Novianto, Sonny; Saga, Sato; Suzuki, Yusuke; Anh, Vo

doi:10.1109/icip.1999.817106

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…As stated in [15] there are a lot of challenges doing this task, and they are mainly caused by staining artifacts, lighting acquisition conditions, and undesired touching objects. Some studies [15] used the saturation channel in the HSV color space, others [16] used multiple gray level automated thresholding over the image's green channel, while others [17] used region growing algorithm [18] over the gray channel of the image.…”

Section: B Epithelium Segmentationmentioning

confidence: 99%

Automated Periodontal Diseases Classification System

Youssif¹,

Gawish²,

Moussa³

2012

IJACSA

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This paper presents an efficient and innovative system for automated classification of periodontal diseases, The strength of our technique lies in the fact that it incorporates knowledge from the patients' clinical data, along with the features automatically extracted from the Haematoxylin and Eosin (H&E) stained microscopic images. Our system uses image processing techniques based on color deconvolution, morphological operations, and watershed transforms for epithelium & connective tissue segmentation, nuclear segmentation, and extraction of the microscopic immunohistochemical features for the nuclei, dilated blood vessels & collagen fibers. Also, Feedforward Backpropagation Artificial Neural Networks are used for the classification process. We report 100% classification accuracy in correctly identifying the different periodontal diseases observed in our 30 samples dataset.

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Section: B Epithelium Segmentationmentioning

confidence: 99%

Automated Periodontal Diseases Classification System

Youssif¹,

Gawish²,

Moussa³

2012

IJACSA

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“…The three last methods mentioned take only into account the color information but not the texture one and for many natural scenes it is very important to consider both. Finally, Maeda et al (Maeda et al, 1999) have proposed a region-growing algorithm that joins color and texture information by applying fuzzy sets, performing a region-growing procedure based on a fixed homogeneity parameter. This method is not adaptative.…”

Section: Introductionmentioning

confidence: 99%

Color and Texture Based Segmentation Algorithm for Multicolor Textured Images

Fondón¹,

Serrano²,

Acha³

2007

Proceedings of the Second International Conference on Computer Vision Theory and Applications

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We propose a color-texture image segmentation algorithm based on multistep region growing. This algorithm is able to deal with multicolored textures. Each of the colors in the texture to be segmented is considered as reference color. In this algorithm color and texture information are extracted from the image by the construction of color distances images, one for each reference color, and a texture energy image. The color distance images are formed by calculating CIEDE2000 distance in the L * a * b * color space to the colors that compound the multicolored texture. The texture energy image is extracted from some statistical moments. The method segment the color information by means of an adaptative N-dimensional region growing where N is the number of reference colors. The tolerance parameter is increased iteratively until an optimum is found and its growth is determined by a step size which depends on the variance on each distance image for the actual grown region. The criterium to decide which is the optimum value of the tolerance parameter depends on the contrast along the edge of the region grown, choosing the one which provides the region with the highest mean contrast in relation to the background. Additionally, this color multistep region growing is texture-controlled, in the sense that an extra condition to include a particular pixel in a region is demanded: the pixel needs to have the same texture as the rest of the pixels within the region. Results prove that the proposed method works very well with general purpose images and significantly improves the results obtained with other previously published algorithm (Fondón et al, 2006).

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“…The three methods mentioned take into account only the color information, but not the texture, and for many natural scenes it is very important to consider both characteristics to get a good segmentation. Finally, Maeda et al 9 proposed a region-growing algorithm that joins color and texture information by applying fuzzy sets, performing a region-growing procedure based on a fixed homogeneity parameter.…”

Section: Chengmentioning

confidence: 99%

Color-texture image segmentation based on multistep region growing

2006

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Abstract.A new method for color image segmentation is proposed. It is based on a novel region-growing technique with a growth tolerance parameter that changes with step size, which depends on the variance of the actual grown region. Contrast is introduced to determine which value of the tolerance parameter is taken, choosing the one that provides the region with the highest contrast in relation to the background. Color and texture information are extracted from the image by means of a novel idea: the construction of a color distance image and a texture energy image. The color distance image is formed by calculating CIEDE2000 distance in the L * a * b * color space. The texture energy image is extracted from some statistical moments. Then, a novel texture-controlled multistep region-growing process is performed for the segmentation. One advantage of the method is that it is not designed to work with a particular kind of images. This method is tested on 80 natural color images of the Corel photo stock collection with excellent results. Numerical evidence of the quality of these results is provided by comparing them with the manual segmentation of five experts and with another color and texture segmentation algorithm.

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Rough and accurate segmentation of natural images using fuzzy region-growing algorithm

Cited by 10 publications

References 13 publications

Automated Periodontal Diseases Classification System

Automated Periodontal Diseases Classification System

Color and Texture Based Segmentation Algorithm for Multicolor Textured Images

Color-texture image segmentation based on multistep region growing

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