In this era, Computerized field in digital image processing needs efficient MRI image with less noise and improved contrast of image. The main process examined and look at different Histogram based enhancement techniques. Histogram equalization analyze on the bases of Magnetic resonance imaging (MRI) furthermore calculate the metrics parameter of histogram techniques. Image enhancement is a procedure of changing or adjusting image in order to make it more suitable for certain applications and is used to enhance or improve contrast ratio, brightness of image, remove noise from image and make it easier to identify. Magnetic resonance imaging (MRI) is an astounding medical technology provide more appropriate information regarding Human brain soft tissue, cancer, stroke and various another diseases. MRI helps doctors to identify the diseases easily. MRI has very low contrast ratio to improve contrast of MRI image we used Histogram equalization technique. In which, Histogram Equalization, Local Histogram Equalization, Adaptive Histogram Equalization and Contrast Limited Adaptive Histogram Equalization techniques are compared.

MRI brain image enhancement using Histogram Equalization techniques

Breast cancer is the revelatory health problem among the world and becomes the most common malignancy in women. In the area of medical research, image processing is quite receptive in applications like mammography, computer-aided detection, breast ultrasound and breast MRI. The physician can examine and even diagnose the tumor with the help of the output in terms of an image produced by these techniques. The best way to improve the prognosis of breast cancer is early detection. X-ray mammography is the primary method used for early detection of the breast cancer. Although mammography is an effective screening tool, it does not provide certain and reliable results for women with dense breasts as well as in women having surgical interventions. Recent researches have shown that MRI proves to be a better alternative to mammography as it does not involve any radiation exposure. The main drawbacks of breast MRI are: Specificity is too low and interpretation is complex and not standardized, which recommended only the screening of high-risk women. The other imaging techniques like ductogram (galactogram), nuclear medicine studies, PET (positron emission tomography) scan, scintimammography (molecular breast imaging), electrical impedance imaging, thermography, optical imaging tests, molecular breast imaging, positron emission mammography are also available for breast cancer detection. So the main objectives of this paper are to represent the comparative analysis of different imaging modalities which are used for breast imaging for detection of cancerous cells and to represent various issues and challenges in the field of breast cancer detection.

Breast Cancer Detection Techniques: Issues and Challenges

In order to get clear information regarding patient it is necessary to enhance medical images like MRI, CT scan, ultrasound etc.  For clinical diagnosis, we have to transmit it through the communication network. During this process information must be protected from malicious users. In this process these images are manipulated, so to protect these images we have to follow some security requirements. In this paper, we are increasing the quality of the image by using enhancement with clahe technique and that enhanced image is watermarked for security purpose by using DWT, SVD transforms with a scaling factor as uniform distribution function. The performance evaluation parameters will give better results for medical as well as under water images. The obtained results are very helpful for integrity of medical images. The technique will provide better response for medical images. This method will give good results in terms of improvement in output, Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).  

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Enhanced Medical Image Watermarking Scheme with CLA-HE& DWT, SVD Transforms

Pelvic Lymph Nodes (PLNs) segmentation and classification are fundamental tools in the medical image analysis of pelvic gynecological cancer such as endometrial and cervical cancer. Often used by the radiologist, PLN classification requires detailed knowledge of the morphological features of PLNs, derived from size, shape, contour and heterogeneous appearance. Accurate PLN segmentation is an essential step in PLN classification. In order to supply the best assessment of a nodal status, semiautomatic and automatic PLN segmentation and classification methods are highly desired as they can strongly capture the wide variability in morphological features and reduce classification errors due to the inter and intra-observer variability, while avoiding the time-consuming for manual delineation of PLN boundary. Nevertheless, semi-automatic segmentation methods require the clinician intervention to select the initial seed point. However, typical semi-automatic PLN segmentation methods might fail due to (1) the intensity inhomogeneity, noise and low contrast in medical images, and (2) the position of the starting point. Thus, the performance of these methods can be enhanced by using a preprocessing-based iterative segmentation approach. Currently, Magnetic Resonance Imaging (MRI) is the most common imaging modality used for staging endometrial and cervical cancer, evaluation of PLN involvement and selection of therapeutic strategy. PLN detection using classic features can be challenging due to the similarity between normal and abnormal PLNs structures. In pelvic cancer and metastatic PLN, Diffusion Weighted (DW)-MRI exhibits brighter areas indicating tumor and metastatic PLN. This paper combines anatomic T2-Weighted (T2-w) imaging with DW imaging. Specifically, we propose a computer-aided pelvic framework, which leverages (1) an ensemble preprocessing method to improve PLN segmentation, (2) the iterative correction of the position of the initial point by executing the segmentation algorithm several times in succession, (3) the fusion of structural and diffusion MRI and, (4) the extraction of morphological features of segmented PLNs (axial T2-w image) as well as intensity feature derived from the fused image for the final classification of PLNs as suspect or non-suspect. Research in the field of PLN detection is important as it can help doctors to better detect cervical and endometrial cancer and decide the appropriate treatment. To the best of our knowledge, this is the first work to segment and classify PLN. Our preprocessing-based iterative segmentation approach significantly (p<0.05) improved comparison segmentation methods, with a segmentation accuracy boosted from 61.37% for the conventional region-growing algorithm to 66.53% for the proposed method. Furthermore, we obtained an average accuracy of 78.50% for pelvic nodule classification.

/pdf/computer-aided-lymph-node-detection-using-pelvic-magnetic-179cghj5rt.pdf

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

This paper presents a new approach for contrast enhancement of spinal cord medical images based on multirate scheme incorporated into multiscale retinex algorithm. The proposed work here uses HSV color space, since HSV color space separates color details from intensity. The enhancement of medical image is achieved by down sampling the original image into five versions, namely, tiny, small, medium, fine, and normal scale. This is due to the fact that the each versions of the image when independently enhanced and reconstructed results in enormous improvement in the visual quality. Further, the contrast stretching and MultiScale Retinex (MSR) techniques are exploited in order to enhance each of the scaled version of the image. Finally, the enhanced image is obtained by combining each of these scales in an efficient way to obtain the composite enhanced image. The efficiency of the proposed algorithm is validated by using a wavelet energy metric in the wavelet domain. Reconstructed image using proposed method highlights the details (edges and tissues), reduces image noise (Gaussian and Speckle) and improves the overall contrast. The proposed algorithm also enhances sharp edges of the tissue surrounding the spinal cord regions which is useful for diagnosis of spinal cord lesions. Elaborated experiments are conducted on several medical images and results presented show that the enhanced medical pictures are of good quality and is found to be better compared with other researcher methods.

An Improved Approach for Contrast Enhancement of Spinal Cord Images based on Multiscale Retinex Algorithm

Image fusion is the process of combining two images into a single image, while retaining the important features of each image. The resulting image contains more information as compared to individual images. Multiple image fusion is an important technique used in military, remote sensing and medical applications.The result of image fusion is a new image that is suitable for human & machine perception. The histogram equalization method is useful in images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to better views of functional and structural parts in MRI and CT images. Different methods of image fusion are described that includes adaptive histogram equalization. Based on this methodology, the system is evaluated and the accurate output will be produced. The fusion algorithm of foreground images and background images is studied. Here, the foreground image is MRI and background image is CT image whereby the fusion rule selection is applied on them.

Fusion of MRI and CT Brain Images by Enhancement of Adaptive Histogram Equalization

Image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images to retaining the important features of each image. The resulting image contains more information as compared to individual images. Multiple image fusion is an important technique used in aerial and satellite imaging, medical imaging, robot vision, digital camera application and battle field monitoring. The histogram equalization method is useful in images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to better views of functional and structural parts in MRI and CT images. Based on this, the removal of noise in the image by using adaptive filtering, the system is evaluated and the accurate output will be produced. The fusion algorithm of foreground images and background images is studied. Here, the foreground image is MRI and background image is CT image.

N. Soniya

Papers

Fusion of MRI and CT Brain Images by Enhancement of Adaptive Histogram Equalization

Fusion of MRI and CT brain images using histogram equalization