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Real-time implementations of image processing algorithms on embedded platforms are gaining importance. In this paper, we propose an Application Specific Integrated Circuit (ASIC) architecture for the perfect reconstruction of images using wavelets with a view to extending this to denoising and feature extraction of images. An architecture that implements the Dual-Tree Wavelet Transform is presented. The architecture features a 128×128 single-port block memory and its addressing schemes, a simple upsampling/downsampling method and a folding and adding mechanism. It is implemented using 180nm technology. The results show perfect reconstruction of 128×128 grayscale images with up to 1-bit error in pixel values when compared to the corresponding input images.

Ki-67 labeling index is a widely used biomarker for the diagnosis and monitoring of cancer. Many automated techniques have been proposed for evaluating Ki-67 index. In this paper, we introduce an integrated deep learning based approach. We use MobileUnet model for segmentation and classification and connected component based algorithm for the estimation of Ki-67 index in bladder cancer cases. The average F1 score is 0.92 and dice score is 0.96. The mean absolute error in the evaluated Ki-67 index is 2.1. We also explore possible pre-processing steps to generalize the segmentation model to at least one another type of cancer. Histogram matching and re-sizing improve the performance in breast cancer data by 12% in F1 score and 8% in dice score.

With the development of modern imaging techniques, every medical examination would result in a huge volume of image data. Analysis, storage and/or transmission of these data demands high compression without any loss of diagnostically significant data. Although, various 3-D compression techniques have been proposed, they have not been able to meet the current requirements. This paper proposes a novel method to compress 3-D medical images based on human vision model to remove visually insignificant information. The block matching algorithm applied to exploit the anatomical symmetry remove the spatial redundancies. The results obtained are compared with those of lossless compression techniques. The results show better compression without any degradation in visual quality. The rate-distortion performance of the proposed coders is compared with that of the state-of-the-art lossy coders. The subjective evaluation performed by the medical experts confirms that the visual quality of the reconstructed image is excellent.

Fixed noise suppression techniques are generally used for speech enhancement in different low power real time systems. In this paper, we propose a modified adaptive system for classification of speech signals and noise reduction based on multi-band techniques. It involves initial identification of incoming speech segments as clean speech, speech in noise or pure noise. For the noisy speech segments, background noise classification is carried out using different wavelet-based feature sets. Noise Reduction system consists of removal of adaptive stationary noise and non-stationary noise based on classified noise type. Simulation results show that the proposed system provides optimal noise reduction and better speech quality with reduced computational complexity in adverse noisy environments.

Medical imaging modalities produce large volume of digital data each day in modern healthcare. Several techniques have been proposed for volumetric medical image data compression. In this paper, we present a novel wavelet-based visually lossless coding scheme for the compression of volumetric magnetic resonance imaging (MRI) and computed tomography (CT) images. A visual model is incorporated in the coder to identify and measure visually irrelevant information. Performance of the compression scheme is further improved by eliminating the slice redundancy. The obtained results show better compression ratio compared to results obtained with pixel-based visually lossless compression technique, without any degradation in visual quality. We compared the performance of proposed technique with standard state of the art compression codecs such as joint photographic experts group-lossless (JPEG-LS), JPEG-2000, JPEG-3D, H.264/MPEG-4 AVC, differential pulse code modulation (DPCM) and medical image lossless compression (MILC). Results show better compression ratio over that of standard lossless compression schemes without any perceivable distortion.