Digital Image Processing

Nimmy Ann Mathew ¹, J. Samuel Manoharan ²

Affiliations
1 Department of Electronics and Communication Engineering in Karunya University, South India, IN
2 Department of Electronics and Communication Engineering, Karunya University, South India, IN

Abstract

Medical image processing has been gaining widespread significance and attention with the advent of new technologies to increase speed and lower the cost and time. At the same time, there has been no compromise in the loss of quality on the processed medical image as they remain to be very sensitive in the sense that even the slightest degradation as a result of the processing would lead to fatal consequences. Numerous techniques have been put forward over the time to make the image under study resilient towards a wide range of attacks especially when some vital information is concealed or embedded inside the host medical image. A robust technique is proposed which is ablt to provide the necessary robustness to the host image. An invisible non blind multi resolution watermarking scheme for medical images using Contourlet transform (CT) is proposed in this paper. The directional features of the Contourlet transform have been utilized to provide choice of embedding location in a hybrid combination with the discrete cosine transform (DCT) to provide the required robustness. The embedded image along with its payload has been tested with a wide range of intentional and unintentional attacks simulating the real time attacks and the results justify its superiority over the existing techniques in terms of robustness. The evaluation has been done using standard metrics like Peak signal to noise ratio (PSNR), Correlation coefficient (CC). Since the proposed method has good resistance to many of the attacks and good perceptibility it is suitable for data hiding in medical image.

Keywords

Geometrical Attacks, Contourlet Transform (CT), Robustness.

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D. Narain Ponraj ¹, Sweety Kunjachan ², P. Poongodi ³, J. Samuel Manoharan ²

Affiliations
1 Department of Electronics and Communication Engineering. Karunya University, IN
2 Department of Electronics and Communication Engineering, Karunya University, IN
3 Department of ECE, PPG Institute of Technology, and Coimbatore, IN

Abstract

Breast cancer is the leading cause of death of women in United States. Modern mammography is the only technique that has demonstrated the ability to detect breast cancer at an early stage and with high sensitivity and specificity. The search for features in this kind of image is complicated by the higher-frequency textural variations in image intensity. The interpretation of mammograms is a skilled and difficult task. But the high rate of false positives in mammography causes a large number of unnecessary biopsies. A characteristic feature of the mammograms is their textured appearance. With this texture extraction the number of false positives can be reduced. The aim of this paper is to review on existing approaches to the texture extraction in the detection of breast cancer. Existing texture analysis algorithms are carefully studied and classified into three categories: texture analysis in the detection of masses, micro calcification, and also in tissue surrounding the region. Different methods of texture extractions can also be done in each category. The identification of glandular tissues in breast X-rays is another important task in assessing left and right breasts images. The appearance of glandular tissue in mammograms is highly variable, ranging from sparse streaks to dense blobs. Fatty regions are generally smooth and dark. Texture analysis provides a flexible approach to discriminating between glandular and fatty regions. Therefore the importance of texture analysis is presented first in this paper. Each approach is reviewed according to its classification, and its merits and drawbacks are outlined. The reviewed results show that many approaches greatly improve the false positive and false negative reduction rates.

Keywords

Breast Cancer, Classification, Malignant, Mammogram.

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