Application of Novel Lossless Compression of Medical Images Using Prediction and Contextual Error Modeling

Conduction of tele-3D-computer assisted operations as well as other telemedicine procedures often requires highest possible quality of transmitted medical images and video. Unfortunately, those data types are always associated with high telecommunication and storage costs that sometimes prevent more frequent usage of such procedures. We present a novel algorithm for lossless compression of medical images that is extremely helpful in reducing the telecommunication and storage costs. The algorithm models the image properties around the current, unknown pixel and adjusts itself to the local image region. The main contribution of this work is the enhancement of the well known approach of predictor blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique. We show that this approach is well suited for medical image data compression. Results obtained with the proposed compression method on medical images are very encouraging, beating several well known lossless compression methods. The predictor proposed can also be used in other image processing applications such as segmentation and extraction of image regions

Application of Novel Lossless Compression of Medical Images Using Prediction and Contextual Error Modeling

Conduction of tele-3D-computer assisted operations as well as other telemedicine procedures often requires highest possible quality of transmitted medical images and video. Unfortunately, those data types are always associated with high telecommunication and storage costs that sometimes prevent more frequent usage of such procedures. We present a novel algorithm for lossless compression of medical images that is extremely helpful in reducing the telecommunication and storage costs. The algorithm models the image properties around the current, unknown pixel and adjusts itself to the local image region. The main contribution of this work is the enhancement of the well known approach of predictor blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique. We show that this approach is well suited for medical image data compression. Results obtained with the proposed compression method on medical images are very encouraging, beating several well known lossless compression methods. The predictor proposed can also be used in other image processing applications such as segmentation and extraction of image regions

CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless Compression of High-Color DICOM Medical Images

Medical image compression is a widely studied field of data processing due to its prevalence in modern digital databases. This domain requires a high color depth of 12 bits per pixel component for accurate analysis by physicians, primarily in the DICOM format. Standard raster-based compression of images via filtering is well-known; however, it remains suboptimal in the medical domain due to non-specialized implementations. This study proposes a lossless medical image compression algorithm, CompaCT, that aims to target spatial features and patterns of pixel concentration for dynamically enhanced data processing. The algorithm employs fractal pixel traversal coupled with a novel approach of segmentation and meshing between pixel blocks for preprocessing. Furthermore, delta and entropy coding are applied to this concept for a complete compression pipeline. The proposal demonstrates that the data compression achieved via fractal segmentation preprocessing yields enhanced image compression results while remaining lossless in its reconstruction accuracy. CompaCT is evaluated in its compression ratios on 3954 high-color CT scans against the efficiency of industry-standard compression techniques (i.e., JPEG2000, RLE, ZIP, PNG). Its reconstruction performance is assessed with error metrics to verify lossless image recovery after decompression. The results demonstrate that CompaCT can compress and losslessly reconstruct medical images, being 37% more space-efficient than industry-standard compression systems.Comment: (8/24/2023) v1a: 16 pages, 9 figures, Word PD

FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition

Transfer learning is a promising technique for medical image classification, particularly for long-tailed datasets. However, the scarcity of data in medical imaging domains often leads to overparameterization when fine-tuning large publicly available pre-trained models. Moreover, these large models are ineffective in deployment in clinical settings due to their computational expenses. To address these challenges, we propose FoPro-KD, a novel approach that unleashes the power of frequency patterns learned from frozen publicly available pre-trained models to enhance their transferability and compression. FoPro-KD comprises three modules: Fourier prompt generator (FPG), effective knowledge distillation (EKD), and adversarial knowledge distillation (AKD). The FPG module learns to generate targeted perturbations conditional on a target dataset, exploring the representations of a frozen pre-trained model, trained on natural images. The EKD module exploits these generalizable representations through distillation to a smaller target model, while the AKD module further enhances the distillation process. Through these modules, FoPro-KD achieves significant improvements in performance on long-tailed medical image classification benchmarks, demonstrating the potential of leveraging the learned frequency patterns from pre-trained models to enhance transfer learning and compression of large pre-trained models for feasible deployment

Dynamic Selection of Suitable Wavelet for Effective Color Image Compression using Neural Networks and Modified RLC

Image Compression has become extremely important today with the continuous development of internet, remote sensing and satellite communication techniques. In general, single Wavelet is not suitable for all types of images. This paper proposes a novel approach for dynamic selection of suitable wavelet and effective Image Compression. Dynamic selection of suitable wavelet for different types of images, like natural images, synthetic images, medical images and etc, is done using Counter Propagation Neural Network which consists of two layers: Unsupervised Kohonen (SOFM) and Supervised Gross berg layers. Selection of suitable wavelet is done by measuring some of the statistical parameters of image, like Image Activity Measure (IAM) and Spatial Frequency (SF), as they are strongly correlated with each other. After selecting suitable wavelet, effective image compression is done with MLFFNN with EBP training algorithm for LL2 component. Modified run length coding is applied on LH2 and HL2components with hard threshold and discarding all other sub-bands which do not effect much the quality (both subjective and objective) (HH2, LH1, HL1 and HH1). Highest CR (191.53), PSNR (78.38 dB), and minimum MSE (0.00094) of still color images are obtained compared to SOFM, EZW and SPIHT

New Watermarking/Encryption Method for Medical Imaging FULL Protection in m-Health

In this paper, we present a new method for medical images security dedicated to m-Health based on a combination between a novel semi reversible watermarking approach robust to JPEG compression, a new proposed fragile watermarking and a new proposed encryption algorithm. The purpose of the combination of these three proposed algorithms (encryption, robust and fragile watermarking) is to ensure the full protection of medical image, its information and its report in terms of confidentiality and reliability (authentication and integrity). A hardware implementation to evaluate our system is done using the Texas instrument C6416 DSK card by converting m-files to C/C++ using MATLAB coder. Our m-health security system is then run on the android platform. Experimental results show that the proposed algorithm can achieve high security with good performance

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging