Earticle

Background: Color texture analysis was applied as a tool for quantitative evaluation of radiation- induced skin injuries. Materials and Methods: We prospectively selected 20 breast cancer patients who underwent whole-breast radiotherapy after breast-conserving surgery. Color images of skin surfaces for irradiated breasts were obtained by using a mobile skin analyzer. The first skin measurement was performed before the first fraction of radiotherapy, and the subsequent measurement was conducted approximately 10 days after the completion of the entire series of radiotherapy sessions. For comparison, color images of the skin surface for the unirradiated breasts were measured similarly. For each color image, six co-occurrence matrices (red-green [RG], red-blue [RB], and green-blue [GB] from color channels, red [R], green [G], blue [B] from gray channels) can be generated. Four textural features (contrast, correlation, energy, and homogeneity) were calculated for each co-occurrence matrix. Finally, several statistical analyses were used to investigate the performance of the color textural parameters to objectively evaluate the radiation-induced skin damage. Results and Discussion: For the R channel from the gray channel, the differences in the values between the irradiated and unirradiated skin were larger than those of the G and B channels. In addition, for the RG and RB channels, where R was considered in the color channel, the differences were larger than those in the GB channel. When comparing the relative values between gray and color channels, the ‘contrast’ values for the RG and RB channels were approximately two times greater than those for the R channel for irradiated skin. In contrast, there were no noticeable differences for unirradiated skin. Conclusion: The utilization of color texture analysis has shown promising results in evaluating the severity of skin damage caused by radiation. All textural parameters of the RG and RB cooccurrence matrices could be potential indicators of the extent of skin damage caused by radiation.

In this paper we are elaborating the concept of disease detection in the human body using the nail image of human fingers and analyzing the data from the image on the basis of nail color and texture. Fingernail has to be detected from the entire hand region using distribution density of the nail color pixels on the surface of nail. The methodology for creating a finger nail detection system involves removing the skin area from shiny/glossy nail portion; this is known as image segmentation concept that separates the specific object. Image segmentation is the method of dividing the image pixels into homogenous region.

Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%.

텍스처 이미지가 다양한 산업 애플리케이션 분야에 널리 사용됨에 따라, 이러한 이미지들의 저작권 보호는 중요한 이슈가 되어왔다. 이러한 이유로, 본 논문은 이미지에 내재한 텍스처 특성을 이용한 칼라 텍스처 이미지 워터마킹 알고리즘을 제안한다. 제안한 알고리즘은 퍼지 클러스터링을 위한 입력으로써 그레이 레벨 동시발생 행렬의 에너지와 동질성 특징을 사용하여 워터마크를 삽입하기 위한 적당한 블록들을 선택한다. 워터마크를 삽입하기 위해 먼저 선택된 블록들에 이산 웨이블릿 변환을 수행하고, 이산 웨이블릿 변환의 서버밴드들의 하나를 선택한다. 그런후에 이 워터마크를 중간 대역의 이산 코사인 변환 계수에 삽입한다. 또한, 본 논문은 워터마크 삽입 후 비인지성과 다양한 형태의 워커마킹 공격에 대해 강인성이 뛰어난 이득 계수들과 이산 웨이블릿 변환의 서버밴드들의 효과를 탐색한다. 모의실험 결과, 제안한 알고리즘은 이득 계수가 42이고 HH 밴드에 워터마크를 삽입하였을 때 높은 PSNR 값 (47.66 dB to 48.04 dB) 및 낮은 M-SVD 값 (8.84 to 15.6)을 얻었다. 또한 제안한 알고리즘은 노이즈 첨가, 필터링, 잘라내기 및 JPEG 압축과 같은 다양한 이미지 처리 공격에서도 높은 상관 값 (0.7193 to 1)을 보였다.

As texture images have become prevalent throughout a variety of industrial applications, copyright protection of these images has become important issues. For this reason, this paper proposes a color-texture image watermarking algorithm utilizing texture properties inherent in the image. The proposed algorithm selects suitable blocks to embed a watermark using the energy and homogeneity properties of the grey level co-occurrence matrices as inputs for the fuzzy c-means clustering algorithm. To embed the watermark, we first perform a discrete wavelet transform (DWT) on the selected blocks and choose one of DWT subbands. Then, we embed the watermark into discrete cosine transformed blocks with a gain factor. In this study, we also explore the effects of the DWT subbands and gain factors with respect to the imperceptibility and robustness against various watermarking attacks. Experimental results show that the proposed algorithm achieves higher peak signal-to-noise ratio values (47.66 dB to 48.04 dB) and lower M-SVD values (8.84 to 15.6) when we embedded a watermark into the HH band with a gain factor of 42, which means the proposed algorithm is good enough in terms of imperceptibility. In addition, the proposed algorithm guarantees robustness against various image processing attacks, such as noise addition, filtering, cropping, and JPEG compression yielding higher normalized correlation values (0.7193 to 1).

Purpose: The net rind pattern and color of melon surface are important for a high market value of melon fruits. The development of the net and color are closely related to the changes in shape, size, and maturing. Therefore, the net and color characteristics can be used indicators for assessment of melon quality. The goal of this study was to investigate the possibility of estimating melon soluble solids content (SSC) and firmness by analyzing the net and color characteristics of fruit surface. Methods: The true color images of melon surface obtained at fruit equator were analyzed with 18 color features and 9 texture features. The partial least squares (PLS) method was used to estimate SSC and firmness in melons using their color and texture features. Results: In sensing melon SSC, the coefficients of determination of validation (${R_v}^2$) of the prediction models using the color and texture features were 0.84 (root mean square error of validation, RMSEV: 1.92 $^{\circ}Brix$) and 0.96 (RMSEV: 0.60 $^{\circ}Brix$), respectively. The ${R_v}^2$ values of the models for predicting melon firmness using the color and texture features were 0.64 (RMSEV: 4.62 N) and 0.79 (RMSEV: 2.99 N), respectively. Conclusions: In general, the texture features were more useful for estimating melon internal quality than the color features. However, to strengthen the usefulness of the color and texture features of melon surface for estimation of melon quality, additional experiments with more fruit samples need to be conducted.