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Background: Direct detection of polyps from colon capsule endoscopy (CCE) videos is an ultimate goal not only for physicians but also for biomedical engineers who are working on automatic internal lesions like polyps. There is also a great enthusiasm among biomedical professionals to make advanced systems for aiding doctors to have a faster and accurate diagnosis by the means of polyp detection from CCE acquired video streams. Such systems must be able to localize polyps correctly and extract the whole lesions from the video frames completely.Material and Methods: In this paper, a new approach toward object-wise polyp detection from CCE frames in a video stream is proposed. The proposed method employs modified region proposal CNNs to localize the existing polyps from CCE acquired video frames and after that a level-set method known as Distance Regularized Level Set Evolution (DRLSE) is employed for automatic model-based segmentation of localized polyps. The pixel-wise detection of polyps is necessary for polyp classification and will help gastroenterologists to determine appropriate prognosis and treatment for the patients.Results and conclusion: The proposed method is trained by the means of an CCE still image dataset which includes manually annotated polyps. The trained network is then applied to CCE video images. The results demonstrate that the proposed method is able to localize and detect polyps both region-wise and pixel-wise with a good rate of accuracy.

Histopathology slides are one of the most applicable resources for pathology studies. As observation of these kinds of slides even by skillful pathologists is a tedious and time-consuming activity, computerizing this procedure aids the experts to have faster analysis with more case studies per day. In this paper, an automatic mitosis detection system (AMDS) for breast cancer histopathological slide images is proposed. In the proposed AMDS, the general phases of an automatic image based analyzer are considered and in each phase, some special innovations are employed. In the pre-processing step to segment the input digital histopathology images more precisely, 2D anisotropic diffusion filters are applied to them. In the training segmentation phase, the histopathological slide images are segmented based on RGB contents of their pixels using maximum likelihood estimation. Then, the mitosis and non-mitosis candidates are processed and hence that their completed local binary patterns are extracted object-wise. For the classification phase, two subsequently non-linear support vector machine classifiers are trained pixel-wise and object-wise, respectively. For the evaluation of the proposed AMDS, some object and region based measures are employed. Having computed the evaluation criteria, our proposed method performs more efficient according to f-measure metric (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) than the methods proposed by other participants at Mitos-ICPR2012 contest in breast cancer histopathological images. The experimental results show the higher performance of the proposed AMDS compared with other competitive systems proposed in Mitos-ICPR2012 contest.

 Abstract—Automatic and reliable extraction of the minutiae from fingerprint images is a critical process in fingerprint matching and a main preprocess for this stage is Thinning. There are a lot of algorithms for fingerprint thinning procedure. All of the previously proposed thinning methods try to thin every ridge due to the content of its central pixel and then extracting minutiae based on some other algorithms for denoising and preventing false minutiae detections at islands or spurities. If an algorithm could thin fingerprint ridges except unrecoverable corrupted regions and also could eliminate noise, it will be considered as a good thinning one and no additional processes are needed before minutiae extraction. The proposed method of this paper has such abilities. The proposed algorithm is implemented by applying four boxes of matrices; each of them thins ridges due to a specific direction; i.e., diagonal, horizontal and vertical directions. The proposed algorithm also is able to thin discrete Latin Characters or symbols. For evaluating the proposed method, several robust and reliable experiments have been employed and the results confirm the higher ability of the proposed method in comparison with the other competing one.

In this paper, an innovative blind watermarking algorithm has been proposed for imagery applications. This algorithm has used the coefficients of the discrete wavelet transform of the host image in the form of super trees to embed the predefined binary watermark in the host image. In this scheme, a pseudo random sequence is generated to determine the exact wavelet super trees used for embedding procedure. In the next step, after choosing the maximum and second maximum amplitude coefficients of each super tree, the distance vector between two coefficients is computed. For embedding bit zero of the specified watermark, the values of the distance vector elements are decreased, while for embedding bit 1, those values will be increased based on the proposed formulas. The experimental results show that the proposed algorithm has significant robustness against image processing attacks, especially JPEG compression and also the PSNR value for the watermarked images generated by the proposed method is more than 42 dB. The watermark bits may be inserted into the host image either in spatial or in the transform domain. It is shown that hiding information in the transform domain will lead to a more robust watermarking system against most of the attacks (6). Among these methods, methods based on discrete wavelet transform (DWT) are of great renown. This is due to specific features of space-frequency localization, multi-resolution display, linear computational complexity and the excellent modeling related to Human Vision System

This paper proposes a lossless data hiding (LDH) scheme on uncompressed video data based on a multi-level histogram shifting mechanism in integer wavelet transform (IWT) domain. The proposed method enables the exact recovery of the original host signal upon extracting the embedded information, if the watermarked image is not affected by any other process. In the proposed scheme, the approximation subband image of the luminance component of a video frame is computed. Then, it is divided into non overlapping blocks. In each block, the differences between the neighboring elements are computed and a histogram is made on the difference values. The secret data are embedded into the blocks based on a multi-level shifting mechanism of the histogram. Experimental results show that proposed scheme can hide a large amount of information within a video frame with a high degree of robustness against H.264/AVC encoding.

In this paper, a robust lossless data hiding scheme on uncompressed video data based on histogram distribution constrained (HDC) scheme is proposed. The proposed method is a reversible data hiding algorithm which enables exact recovery of the original video without any distortion after extracting hidden information, if the watermarked video is not affected by any other process. The algorithm is robust against H.246/AVC video compression. In the proposed method, the luminance components of video frames are used to embed the watermark data bits. The luminance component is divided into non-overlapping blocks and the arithmetic difference of each block is computed. In the next step, the hiding data are embedded into the blocks by shifting the arithmetic difference values. The performance of the proposed algorithm is evaluated with respect to imperceptibility, robustness against H.264 video coding and data payload capacity by simulations. Simulation results demonstrate a high performance for the proposed method.

The privacy and integrity protection of digital documents specially the case of biomedical images is a vital subject in the current world of telecommunication and digital multimedia exchanges. In this paper, a modified semi robust digital image watermarking method with tamper detection and recovery ability is proposed. For this purpose, the proposed method comprises two stages. In the first stage, a specific order of the integer wavelet coefficient of the original image forms the watermark which prepares both tamper detection and recovery ability. These abilities are created by a specific parsing of watermark bits named as dual watermarking. In the second stage of the proposed method, watermark embedding and extraction procedures are done. To be more robust, a Convolutional Error Correction Code is employed in the watermark establishment process. In the extraction process, the probable tamper will be detected and the original image shall be recovered. This method is also improved to be adopted for reversible ROI recovery of medical images even in the presence of some kind of intentional and unintentional attacks. The experimental results show that the proposed method has a high performance in the case of tamper detection and is able to recover original images from noisy ones.