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Today, the histological study of biopsy specimens is still used to diagnose brain tumors (BTs). This existing procedure is intrusive, arduous, and liable to mistakes. These downsides highlight the standing of employing a completely computerized process for identifying the evolution of tumors in the brain. A primary BT affects an estimated 0.7 million persons in the United States now and more are expected to be detected in the coming years. The ability to categorize magnetic resonance (MR) brain images into ordinary and pathological categories has the boundless ability to significantly diminish the burden on the radiologist. Pre‐processing, extraction, and reduction of features along with their classification are the parameters of statistical‐based methodologies that have been frequently used for this purpose. In this work, an enhanced framework for the identification of the BT is proposed using discrete wavelet transform (DWT), deep convolutional network (DCN), and machine learning (ML). As DWT is primarily used for image compression and denoising applications however in the presented research work it has been utilized for extricating pivotal features from the MR images using the feature fusion technique. DCN is also utilized for the extraction of pivotal deep features which are then combined with the wavelet‐based features for the purpose of BT identification. The classification of tumorous and non‐tumorous MR images is done using ML applications. The results obtained from the proposed model exhibit an utmost accuracy of 99.5% with an area under curve of 1 in identifying tumorous and non‐tumorous MR images as compared to various state‐of‐the‐art models. The proposed model can be efficiently used for assisting radiologists and medical experts in validating their decisions for BT identification. [ABSTRACT FROM AUTHOR]

A chaos-based cryptosystem for fractal image coding is proposed. The R'enyi chaotic map is employed to determine the order of processing the range blocks and to generate the keystream for masking the encoded sequence. Compared with the standard approach of fractal image coding followed by the Advanced Encryption Standard, our scheme offers a higher sensitivity to both plaintext and ciphertext at a comparable operating efficiency. The keystream generated by the R'enyi chaotic map passes the randomness tests set by the United States National Institute of Standards and Technology, and so the proposed scheme is sensitive to the key. [ABSTRACT FROM AUTHOR]

Modern fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular biorthogonal 9-7 discrete wavelet transform. Multiresolution analysis tools have been successfully applied to fingerprint image compression for more than a decade; we propose a novel fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization. Wave atoms decomposition has been specifically designed for enhanced representation of oscillatory patterns and to convey precise temporal and spatial information. Our proposed compression scheme is based upon multistage vector quantization of processed wave atoms representation of fingerprint images. Wave atoms expansion is processed using mathematical morphological operators to emphasize and retain significant coefficients for transmission. Quantized information is encoded using arithmetic entropy scheme. The proposed image compression standard outperforms other well established methods and achieves PSNR gain up to 8.07 dB in comparison to FBI's wavelet scalar quantization. Data mining, law enforcement, border security, and forensic applications can potentially benefit from our compression scheme. [ABSTRACT FROM AUTHOR]

The US National Institute of Standards and Technology (NIST) has published a revised biometric standard that expands the type and amount of information that forensic scientists can share across their international networks to identify victims or solve crimes. [ABSTRACT FROM AUTHOR]

The article elaborates on the significance of the Portable Document Format (PDF) Shrink utility developed by Apago, PDF developer, in the U.S. It was found that the package will optimize PDF files to get accurate viewing on the Apple iPhone and iTouch devices. According to Dwight Kelly, president of Apago, one of the problems experienced by these devices is that JPEG 200 image compression is not supported by the Apple mobile Preview application.

The article reports that the U.S. Patent and Trademark Office has issued U.S. Patent No. 7,184,603, titled “System and method for lossless compression of digital images” to Smith MicroSoftware Inc. The patent covers a method for enhancing image compression by applying adaptive techniques to achieve significant compression gains over currently known methods. The JPEG image compression technology reduces the size of JPEG photographs and images up to 30 percent with no loss in image quality.