In this paper, we propose a novel encryption algorithm for medical images. Our algorithm is based around ad-vanced encryption standard (AES), however, its simple algebraic structure and the encryption process of every block may become a major concern in security. This is because the block-wise nature of implementation can pass the features of the actual image to visible in the encrypted image. We addressed this issue by introducing two new encryption layers i.e., Fractional Discrete Cosine Transform (FrDCT) and Arnold Transform. unlike the block-wise AES, these two layers are applied image-wide. together this cascade provides excellent security and key sensitivity while preserving the quality of the medical images. The proposed method has been compared with contemporary methods in both quantitative and qualitative manner which further validated our ideas. Even slight change in keys$(\Delta\approx\pm 0.001)$causes a failure in decryption. This shows the robustness of the proposed algorithm.

Abstract In this paper, a novel color image encryption scheme based on fractional discrete cosine transform (FrDCT) and discrete wavelet transform (DWT) with interplane arrangements in the discrete orthonormal Stockwell transform (DOST) domain is presented. Color images are encrypted using the keys of FrDCT and coefficients of interplane arrangement of DWT sub‐bands. These keys are applied on three independent planes, that is, R, G, and B. To ensure the correct decryption of the encrypted image, every key need to be clearly understood in the correct sequence and its exact values. FrDCT has the advantageous feature of storing real‐valued coefficients, and the energy of the image can be represented with very few coefficients. Therefore, transmitting and storing the encrypted image becomes easy. FrDCT may be considered a real‐valued cosine version of fractional Fourier transform (FrFT), which is used successfully for storage and transmission due to its property of compacting energy distribution. Furthermore, for more robustness, interplane arrangements are explored. The concept of interplane arrangements has not been substantially reported in the existing literature. The comparison of the technique with other similar encryption techniques available in the literature has been carried out by performing different experiments to examine the efficacy of the proposed method. It is found that the proposed method outperforms other existing techniques.

In any unique and dynamic climate, for example, rain and snow, it causes varieties in local intensities of the rainy pictures. Such changes can remarkably influence the frameworks that rely upon highlights of the picture for object recognition, tracking, object detection, segmentation etc. Accordingly, there is a requirement for quick and precise detection [1]-[3] and evacuation of rain streaks or the marks of snow. Here, we build up a strategy for diagnosis and dismissal of rain components. Rain components have a higher intensity compare to the non-rain component of the image. This property has been utilized to recognize the rain components in a picture. To decrease the false alarm cases, we use the attributes of raindrops, (for example, major and minor axis) and recognize the identified rain and non-rain component. Correction of the color is utilized to reestablish shading of rain-component with the color of the background. We exhibit that our proposed technique performs well for rain-component identification with more prominent precision compare to other state-of-the-art methods.

In this study, the authors propose a novel technique for medical image watermark detection using the concept of the fractional differentiator (FD). The feature of FD as a non‐linear high‐pass filter helps in watermark detection. In the region of non‐interest, the watermark image has been added in a mid‐band frequency range of the discrete cosine transform coefficients of different blocks by generating direct spread spectrum sequence. Their scheme produces noise‐free watermarked medical images. Furthermore, they derive the test statistics of the proposed detector, which is characterised by the fractional order q . The average errors in pixels (PEs), peak‐signal‐to‐noise ratio (PSNR), structural similarity index (SSIM) and cross‐correlation coefficient (CC) have been used to quantify the capability of the proposed technique over some state‐of‐the‐art techniques. The proposed technique shows that at a particular value of fractional order q , there is a significant reduction in average PEs. It causes an increment in PSNR, SSIM and CC. The proposed technique is tested on a large number of medical images and it is found that their proposed technique works better or comparable with other state‐of‐the‐art techniques.

Pectoral muscle segmentation is a very important step in any Computer Aided Detection (CAD) system, for automatic detection of different possible signs of malignancy in the mammographic image. Presence of high‐density glandular tissues overlapping pectoral muscle boundary, presence of pectoral muscle of small size and low density, presence of pectoralis minor etc., poses a great challenge for researchers working on the problem of automatic pectoral muscle boundary detection. Gabor filter has been used by many researchers to detect textural edges. The ability that it can be tuned along a particular direction, makes it a very important image processing tool. In the proposed research work a Gabor filter tuned in the direction of pectoral muscle boundary is applied on the region of interest (ROI) containing pectoral muscle. Finally, the magnitude and phase response of Gabor filtered ROI image along with an edge‐connect and region merge algorithm is used to detect pectoral muscle boundary. The proposed method is validated using 200 mammograms from the miniMIAS database and 200 mammograms from INbreast database. With an accuracy of 97.50% for miniMIAS database and 94.50% for INbreast database, the proposed method outperforms many state‐of‐the‐art methods which show its suitability for CAD Systems.

This paper introduces a novel video stabilization technique for combined rotational and translational motion using integral frame projections. In the proposed Frame Projection Warping (FPW) method, the normalized intensity projection curves of two consecutive frames are warped using dynamic time warping, to get the relative shift between them. Rotational and vertical motion estimation involves partitioning of frame in to two halves and their corresponding estimated motions are then utilized for respective rotational angle and vertical shift estimation. This technique uses the human perception for analyzing the rotation in terms of vertical displacement of two halves of frame. The proposed technique is tested over various hand recorded videos. The results show better performance of FPW over various existing intensity based techniques. This technique also gives better accuracy in case of frame blurring, which is a serious cause of wrong motion estimation. The performance of the proposed technique is measured in terms of interframe transformation fidelity and processing time.

In this study, a dynamic stochastic resonance (DSR)‐based technique in spatial domain has been proposed for the enhancement of dark‐ and low‐contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low‐contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise‐induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double‐well potential with the intensity values of a low‐contrast image. Optimum output is ensured by adaptive computation of performance metrics – relative contrast enhancement factor ( F ), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single‐scale retinex, multi‐scale retinex, modified high‐pass filtering, edge‐preserving multi‐scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR‐based technique has also been illustrated.

A contrast enhancement technique using scaling of internal noise of a dark image in discrete cosine transform (DCT) domain has been proposed in this paper. The mechanism of enhancement is attributed to noise-induced transition of DCT coefficients from a poor state to an enhanced state. This transition is effected by the internal noise present due to lack of sufficient illumination and can be modeled by a general bistable system exhibiting dynamic stochastic resonance. The proposed technique adopts a local adaptive processing and significantly enhances the image contrast and color information while ascertaining good perceptual quality. When compared with the existing enhancement techniques such as adaptive histogram equalization, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, multi-contrast enhancement, multi-contrast enhancement with dynamic range compression, color enhancement by scaling, edge-preserving multi-scale decomposition and automatic controls of popular imaging tool, the proposed technique gives remarkable performance in terms of relative contrast enhancement, colorfulness and visual quality of enhanced image.

A dynamic stochastic resonance (DSR)-based technique in discrete wavelet transform (DWT) domain for noise suppression in digital images has been proposed in this paper. The initial results on investigation of this concept for denoising of images corrupted by gaussian noise have been presented. Though traditionally noise is considered as undesirable, it has been utilized in the proposed technique to reduce its own effect. In the iterative DSR step, an input noisy image is subjected to independent noise of different standard deviations that iteratively tunes the detail wavelet coefficients, such that the overall effect is the suppression of the degradation due to its own noise. The results are quantified in terms of Noise Mean Value (NMV), Noise Standard Deviation (NSD), and Mean Square Difference (MSD). When compared with the conventional techniques for gaussian denoising, such as gaussian low pass filtering, and soft thresholding of wavelet coefficients, the DSR-based technique is found to give marginally better noise reduction in most of the cases.

In this paper, a novel dynamic stochastic resonance (DSR)-based non-blind watermark extraction technique has been proposed for robust extraction of a grayscale watermark. The watermark embedding has been carried out using singular value decomposition (SVD). Dynamic stochastic resonance has been strategically used to improve the robustness of the extraction algorithm by utilizing the noise added during attacks itself. Resilience of this technique to attacks has been tested in the presence of various noise, geometrical, enhancement, compression and filtering attacks. Using the DSR-based proposed extraction algorithm, a very robust extraction of watermark can be done without trading-off with visual quality of the watermarked image. Performance of the proposed technique has also been compared with the plain SVD-based and hybrid DCT-SVD based technique and is found to give better performance.