Zheng Wei Yang Hua Sun Hui-sheng Fan Hong-qi presents[11] a technique for for processing and enhancing x-ray image using morphological operation. The concept of image enhancement has been extended to the regime of multiscale mathematical morphology. Structuring element in this method is multiscale. Bright and dark features at various scales of x-ray image are extracted using multiscale (white or black) transformation. These multiscale features are combined to reconstruct the final modified image. Therefore the contrast of x- ray image is enhanced locally and the features of the original image are clearer. Morphological difference towers are bulk to implement the method. The proposed algorithm has been executed on the x-ray image for testing its result. Some images with other standard or improved methods for contrast enhancement are given in the paper. Compared with these images multiscale mathematical morphological X-ray image enhancement technique has achieved a better visual effect. Multiscale mathematical morphology can be used in x-ray image process[11].
3.2. X-ray images enhancement through adaptive filters and human visual system model properties
M.M. Hadhoud proposes [12] the employ of an adaptive x-ray image enhancement structure that develops the contrast enhancement of X-ray images. X-ray images are of bad quality and are usually difficult to interpret visually. The human visual system properties considered are its adaptive environment, multichannel instrument and greater value of nonlinearity. The method for X-ray image enhancement is multichannel, nonlinear and adaptive, combines with homomorphic processing and adaptive filters. The image vision model used in this method is simply the log in combination with the adaptive system, but it suffices to enhance the quality of interest. In [13], they provided a method of image contrast enhancement, based on processing in homomorphic domain, using a combination of fixed HP and fixed LP filters, The use of adaptive filters which changes characteristics according to the changes in the image statistics in combination with homomorphic processing coincides with the HVS model properties before Adding some simple constraints on the coefficients of the weight matrix, the filter changes characteristics to an adaptive High Pass Filter or to an adaptive Laplacian operator [13]. Any other method of adaptive filters is also applicable. This method also, proposes a new contrast enhancement method with multiple HP channels, in both spatial and homomorphic domains. The method consists of following steps: 1)Contrast Enhancement using Homomorphic Transformation. 2) Multiple HP channels for contrast enhancement 3) Spatial domain Single HP channel
contrast enhancement 4) Adapting contrast enhancement.
X-ray radiography is the most common methods of radiological examinations and detection of some diseases, such as cancer. The input images are of bad quality and usually interpreted wrongly visually. The HVS behavior which is adaptive in nature, multichannel, and is highly nonlinear, must be considered. The proposed technique considers the properties of the HVS model. The adaptive filters change characteristics according to the changes in the image. The application is nonlinear during the use of homomorphic processing and the adaptation of contrast enhancement parameters a and b to the changes in the input image local characteristics and contrast. This method of adaptive parameters calculation controls the enhancement locally. The system is multichannel through the use of multiple HP channels with different HP filter sizes. The HP filters with large sizes, and the averaging of the three outputs of the three HP channels, causes great noise reduction in the output image. The method of multiple HP channels is shown to be very simple, useful and avoids the fine points blurring and noise amplification. In addition to the advantages explained before, the adaptive method does not suppose any information about the image individuality or statistics. Our conclusions are that the methods of image enhancement using adaptive filters are very useful and outperform fixed filter methods[12].
3.3. X-ray image enhancement using dyadic wavelet transform
This method was introduced by Zhihua Qi1, Li Zhang1, Yuxiang Xing1, Shuanglei Li2 [15].They proposed a method of image enhancement. X-ray images are often of low contrast due to the subtle distinction of attenuation coefficients and scatter effect, which makes it difficult to distinguish signals from background. This paper proposes an image enhancement algorithm using the multi-scale dyadic wavelet transform. The algorithm includes two steps: 1) In order to enhance contrast, a non-linear mapping is performed on wavelet coefficients at different scales. Inhibition factors are then applied to reduce the impact of scatter; 2) In order to reduce the negative impact of noise amplification from the first step, wavelet coefficients are divided into two categories: asymmetrical coefficients ,edge-related and regular coefficients, and then filtered with different schemes for each category. Experimental results demonstrate that this algorithm effectively enhance the contrast of x-ray images[15].
3.4 phase-contrast x-ray image enhancement of corrosion damage
According to B. Zoofan, J.-Y. Kim and S. I. Rokhlin [16] in this method x-ray image enhancement is defined as Conventional radiography is based on absorption of the transmitted X-rays in a sample to produce an image. The image contrast is obtained due to the variation of sample density and thickness. To allow X-ray penetration through structural materials one needs to increase the X-ray voltage which reduces the X-ray absorption and image contrast and therefore flaw detect ability. Thus for detection of corrosion the image contrast may be insufficient. By utilizing X-ray refraction one may obtain the X-ray phase pattern in the image improving the image contrast and flaw detect ability. This paper describes an experimental phase contrast enchantment of artificial pits in Al and made a quantitative learning of the effect. The experimental method use to accomplish phase contrast in the hard X-ray executive is described for system to nondestructive appraisal of equipment. The absorption-contrast x-ray images are compared with phase-contrast x-ray images representing enhancement of x-ray image quality. The phase-contrast imaging enhances contrast, enhanced edge description and X-ray phase information[16].
This paper presents theoretical and experimental aspects of the enhancing phase-contrast X-ray imaging method using a micro-focus X-ray source as it applicable to early detection of corrosion pits. Diffraction of polychromatic finite source radiation is determined to simulate the experimental conditions used in the micro focal X-ray imaging. The experimental and modeling results are compared showing good agreement. In most prior method, the phase-contrast X-ray imaging technique has been used to image light materials such as biological tissue. In this method, the phase-contrast technique is demonstrated for aluminum samples. The results show significant contrast improvement and the image edge enhancement. This is promising for application of the method to nondestructive evaluation of corrosion in metals[16].
3.5. Structural Enhancement of Digitalized Bones X-ray Images
Using Gaussian Higher Order Derivative
Raka Kundu, Ratnesh Kumar, Biswajit Biswas,Amlan Chakrabarti [17] proposed
A novel method for enhancement of digital X-ray images of bones is presented in this paper. It has come to observation that the proposed method based on the Gaussian higher order derivative shows an appreciable enhancement of edges in digital X-ray images of bones that can be used for detection of various bone deformities and also for the superior understanding of the bone configuration. We have achieved a level of improvement in distinguishing the bone information from the other parts of the digital X-ray images[17].
Due to lack of sharpness, digital X-ray images sometimes do not hold enough information for medical diagnosis. Here, we have undergone a process of image enhancement of bone structures in digital X-ray images. The basic common method of sharpening is addition or subtraction of Laplacian image to the original input image. Therefore a study was carried out on the third order Gaussian operator for increasing the sharpness of the digital bone X-ray image. The higher order Gaussian operators are easy to realize but, are very sensitive to noise. So, prior to application of the higher order Gaussian operator there is need of smoothing the image by noise removal filter. Here, in this paper we mainly focus our research on the derivation of Gaussian higher order derivative operator and its use in highlighting the regions of bones of digital X-ray image by detection of meaningful edges of the image. The method is consisting of two steps: 1) Proposed Gaussian Operator Algorithm 2) X-ray image sharpening.The bones from the soft tissues are easily distinguishable. The new image formed by addition of the higher order Gaussian image provides more information than the normal digital X-ray image. This method proposes an efficient technique of generating enhanced images from the given digital bone X-ray images. The results show that this is also a useful technique for identifying contours in bone images which has a numerous applications in understanding and diagnosing bone deformities[17].
