• Journal of Electrical Engineering and Technology
• /
• v.12 no.1
• /
• pp.436-442
• /
• 2017

A novel method of Skin lesion segmentation based on the combination of Texture and Neural Network is proposed in this paper. This paper combines the textures of different pixels in the skin images in order to increase the performance of lesion segmentation. For segmenting skin lesions, a two-step process is done. First, automatic border detection is performed to separate the lesion from the background skin. This begins by identifying the features that represent the lesion border clearly by the process of Texture analysis. In the second step, the obtained features are given as input towards the Recurrent Echo state neural networks in order to obtain the segmented skin lesion region. The proposed algorithm is trained and tested for 862 skin lesion images in order to evaluate the accuracy of segmentation. Overall accuracy of the proposed method is compared with existing algorithms. An average accuracy of 98.8% for segmenting skin lesion images has been obtained.

• Korean Journal of Human Ecology
• /
• v.23 no.2
• /
• pp.317-328
• /
• 2014

In this study, differences of texture image and preference for men's suit fabrics according to mechanical properties, hand and fabric information were investigated. 55 subjects evaluated texture image and preference of 12 kinds of wool blended fabrics. For statistical analysis, t-test and pearson correlation coefficients were used. The results were as follows: Most of mechanical properties effected on texture images, and bending property and shearing property were effected on tactile preference and purchasing preference. For hand, objective hand values showed correlations with subjective texture images and preferences, but THV had almost no correlations. In sensory images according to presence of fabric information, fabrics were evaluated thinner, lighter, more pliable and smooth by cognition of wool blending ratio. For sensibility images, fabrics were evaluated more refined, intellectual, dignified and less practicable after recognize of wool blending ratio. In preferences, tactile preference was increased and purchasing preference was decreased after recognize fabric information. Therefore, significant differences of texture image and preference were observed according to presence of fabric information.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.11
• /
• pp.3592-3602
• /
• 2000

Image processing consist of image analysis and classification. The one is extracting of feature value in the image. The other is segimentationof image that have same properiv. A novel approach for the analysis and classification of tezture images based on statistical texture prunitive estraction are proposed. In this approach, feature vector extracting is based on stalisucal method using apatial dependence of grey level and use general lexture proerty. In is advantageous that not effiected on structure and type of lexture. These components describe the amount of roughness and softness of texture images Two leatures. Moment and Homogeneity, are componted from GLCM(gray level co-occurrence matrices) of the lexture promitive to charactenize statisical properties of the image. We show the successful experimental results by considerationof these two components fro the analysis and classificationto regular and irregular texture images.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.1
• /
• pp.49-54
• /
• 2016

Regarding global and local factors of a set of features, a given single image or multiple images is a common approach in image processing. This paper introduces an application of an adaptive version of non-local filter whose original version searches non-local similarity for removing noise. Since most images involve texture partner in both foreground and background, extraction of signified regions with texture is a challenging task. Aiming to the detection of visual attention regions for images with texture, we present the contrast analysis of image patches located in a whole image but not nearby with assistance of the adaptive filter for estimation of non-local divergence. The method allows extraction of signified regions with texture of images of wild life. Experimental results for a benchmark demonstrate the ability of the proposed method to deal with the mentioned challenge.

• Journal of Korea Multimedia Society
• /
• v.20 no.7
• /
• pp.994-1003
• /
• 2017

Ground-glass opacity nodules(GGNs) in chest CT images are associated with lung cancer, and have a different malignant rate depending on existence of solid component in the nodules. In this paper, we propose a method to classify pure GGNs and part-solid GGNs using multiview images and texture analysis in pulmonary GGNs with solid components of 5mm or smaller. We extracted 1521 features from the GGNs segmented from the chest CT images and classified the GGNs using a SVM classification model with selected features that classify pure GGNs and part-solid GGNs through a feature selection method. Our method showed 85% accuracy using the SVM classifier with the top 10 features selected in the multiview images.

• Korean Journal of Radiology
• /
• v.20 no.4
• /
• pp.558-568
• /
• 2019

Objective: To evaluate whether computed tomography (CT) reconstruction algorithms affect the CT texture features of the liver parenchyma. Materials and Methods: This retrospective study comprised 58 patients (normal liver, n = 34; chronic liver disease [CLD], n = 24) who underwent liver CT scans using a single CT scanner. All CT images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR) (iDOSE⁴), and model-based IR (IMR). On arterial phase (AP) and portal venous phase (PVP) CT imaging, quantitative texture analysis of the liver parenchyma using a single-slice region of interest was performed at the level of the hepatic hilum using a filtration-histogram statistic-based method with different filter values. Texture features were compared among the three reconstruction methods and between normal livers and those from CLD patients. Additionally, we evaluated the inter- and intra-observer reliability of the CT texture analysis by calculating intraclass correlation coefficients (ICCs). Results: IR techniques affect various CT texture features of the liver parenchyma. In particular, model-based IR frequently showed significant differences compared to FBP or hybrid IR on both AP and PVP CT imaging. Significant variation in entropy was observed between the three reconstruction algorithms on PVP imaging (p < 0.05). Comparison between normal livers and those from CLD patients revealed that AP images depend more strongly on the reconstruction method used than PVP images. For both inter- and intra-observer reliability, ICCs were acceptable (> 0.75) for CT imaging without filtration. Conclusion: CT texture features of the liver parenchyma evaluated using the filtration-histogram method were significantly affected by the CT reconstruction algorithm used.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.9
• /
• pp.1428-1435
• /
• 1989

In this paper we propose a new algorithm which extracts spatial arrangement information of texture elements in structural textures. In the proposed algorithm, by the use of projection information in several directions obtained from the texture image we can get two directions which determine the texture structure and the parallelogram grid which isolates texture elements. The isolated texture elements are analyzed and used to synthesize texture images. Computer simulation shows that the proposed method can extract proper spatial structure of the texture element even when the texture image is highly corrupted by additive noise.

• Science of Emotion and Sensibility
• /
• v.8 no.2
• /
• pp.117-128
• /
• 2005

The purpose of this study was to clarify the effects of constituent characteristics and the mechanical properties by KES-FB system on the visual texture images and preference of men's suit fabrics. Twenty different kinds of black fabrics, which were used mainly for the spring and summer men's suits, were selected and the subjective evaluation of the visual texture images was tested. Sixty experts in department of clothing and apparel industries participated in the subjective evaluation. Factor analysis showed visual texture images were classified into 4 categories : 'bulkiness', $'extensibility{\cdot}rapability'$, 'flexibility' and 'smoothness'. All of relationships were established between the mechanical properties and the visual texture images except the relation between 'flexibility' and bending properties. The significant factors affecting preference were 'smoothness', compression energy(WC), fabric count and 'flexibility', As the fabrics had higher value for 'smoothness' and 'flexibility' , and lower ones for compression energy and fabric count, they tended to be more preferred.

• Journal of Information Processing Systems
• /
• v.13 no.3
• /
• pp.464-475
• /
• 2017

In this paper, we present a new texture image retrieval method which combines color and texture features extracted from images by a set of multi-resolution multi-direction (MRMD) filters. The MRMD filter set chosen is simple and can be separable to low and high frequency information, and provides efficient multi-resolution and multi-direction analysis. The color space used is HSV color space separable to hue, saturation, and value components, which are easily analyzed as showing characteristics similar to the human visual system. This experiment is conducted by comparing precision vs. recall of retrieval and feature vector dimensions. Images for experiments include Corel DB and VisTex DB; Corel_MR DB and VisTex_MR DB, which are transformed from the aforementioned two DBs to have multi-resolution images; and Corel_MD DB and VisTex_MD DB, transformed from the two DBs to have multi-direction images. According to the experimental results, the proposed method improves upon the existing methods in aspects of precision and recall of retrieval, and also reduces feature vector dimensions.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.23-28
• /
• 2002

Among effective feature extraction methods from the small-patched image set, independent component analysis (ICA) is recently well known stochastic manner to find informative basis images. The ICA simultaneously learns both basis images and independent components using high order statistic manners, because that information underlying between pixels are sensitive to high-order statistic models. The topographic ICA model is adapted in our experiment. This paper deals with an unsupervised classification strategies using learned ICA basis images. The experimental result by proposed classification technique shows superior performance than classic texture analysis techniques for the panchromatic KOMPSAT imagery.