Efficient Encoding of Wireless Capsule Endoscopy Images Using Direct Compression of Colour Filter Array Images

Since its invention in 2001, wireless capsule endoscopy (WCE) has played an important role in the endoscopic examination of the gastrointestinal tract. During this period, WCE has undergone tremendous advances in technology, making it the first-line modality for diseases from bleeding to cancer in the small-bowel. Current research efforts are focused on evolving WCE to include functionality such as drug delivery, biopsy, and active locomotion. For the integration of these functionalities into WCE, two critical prerequisites are the image quality enhancement and the power consumption reduction. An efficient image compression solution is required to retain the highest image quality while reducing the transmission power. The issue is more challenging due to the fact that image sensors in WCE capture images in Bayer Colour filter array (CFA) format. Therefore, standard compression engines provide inferior compression performance. The focus of this thesis is to design an optimized image compression pipeline to encode the capsule endoscopic (CE) image efficiently in CFA format. To this end, this thesis proposes two image compression schemes. First, a lossless image compression algorithm is proposed consisting of an optimum reversible colour transformation, a low complexity prediction model, a corner clipping mechanism and a single context adaptive Golomb-Rice entropy encoder. The derivation of colour transformation that provides the best performance for a given prediction model is considered as an optimization problem. The low complexity prediction model works in raster order fashion and requires no buffer memory. The application of colour transformation yields lower inter-colour correlation and allows the efficient independent encoding of the colour components. The second compression scheme in this thesis is a lossy compression algorithm with a integer discrete cosine transformation at its core. Using the statistics obtained from a large dataset of CE image, an optimum colour transformation is derived using the principal component analysis (PCA). The transformed coefficients are quantized using optimized quantization table, which was designed with a focus to discard medically irrelevant information. A fast demosaicking algorithm is developed to reconstruct the colour image from the lossy CFA image in the decoder. Extensive experiments and comparisons with state-of-the-art lossless image compression methods establish the superiority of the proposed compression methods as simple and efficient image compression algorithm. The lossless algorithm can transmit the image in a lossless manner within the available bandwidth. On the other hand, performance evaluation of lossy compression algorithm indicates that it can deliver high quality images at low transmission power and low computation costs

DESIGN AND IMPLEMENTATION OF AN EFFICIENT IMAGE COMPRESSOR FOR WIRELESS CAPSULE ENDOSCOPY

Capsule endoscope (CE) is a diagnosis tool for gastrointestinal (GI) diseases. Area and power are the two important parameters for the components used in CE. To optimize these two parameters, an efficient image compressor is desired. The mage compressor should be able to sufficiently compress the captured images to save transmission power, retain reconstruction quality for accurate diagnosis and consumes small physical area. To meet all of the above mentioned conditions, we have studied several transform coding based lossy compression algorithms in this thesis. The core computation tool of these compressors is the Discrete Cosine Transform (DCT) kernel. The DCT accumulates the distributed energy of an image in a small centralized area and supports more compression with non-significant quality degradation. The conventional DCT requires complex floating point multiplication, which is not feasible for wireless capsule endoscopy (WCE) application because of its high implementation cost. So, an integer version of the DCT, known as iDCT, is used in this work. Several low complexity iDCTs along with different color space converters (such as, YUV, YEF, YCgCo) were combined to obtain the desired compression level. At the end a quantization stage is used in the proposed algorithm to achieve further compression. We have analyzed the endoscopic images and based on their properties, three quantization matrix sets have been proposed for three color planes. The algorithms are verified at both software (using MATLAB) and hardware (using HDL Verilog coding) levels. In the end, the performance of all the proposed schemes has been evaluated for optimal operation in WCE application

Capsule endoscopy system with novel imaging algorithms

Wireless capsule endoscopy (WCE) is a state-of-the-art technology to receive images of human intestine for medical diagnostics. In WCE, the patient ingests a specially designed electronic capsule which has imaging and wireless transmission capabilities inside it. While the capsule travels through the gastrointestinal (GI) tract, it captures images and sends them wirelessly to an outside data logger unit. The data logger stores the image data and then they are transferred to a personal computer (PC) where the images are reconstructed and displayed for diagnosis. The key design challenge in WCE is to reduce the area and power consumption of the capsule while maintaining acceptable image reconstruction. In this research, the unique properties of WCE images are identified by analyzing hundreds of endoscopic images and video frames, and then these properties are used to develop novel and low complexity compression algorithms tailored for capsule endoscopy. The proposed image compressor consists of a new YEF color space converter, lossless prediction coder, customizable chrominance sub-sampler and an efficient Golomb-Rice encoder. The scheme has both lossy and lossless modes and is further customized to work with two lighting modes – conventional white light imaging (WLI) and emerging narrow band imaging (NBI). The average compression ratio achieved using the proposed lossy compression algorithm is 80.4% for WBI and 79.2% for NBI with high reconstruction quality index for both bands. Two surveys have been conducted which show that the reconstructed images have high acceptability among medical imaging doctors and gastroenterologists. The imaging algorithms have been realized in hardware description language (HDL) and their functionalities have been verified in field programmable gate array (FPGA) board. Later it was implemented in a 0.18 μm complementary metal oxide semiconductor (CMOS) technology and the chip was fabricated. Due to the low complexity of the core compressor, it consumes only 43 µW of power and 0.032 mm2 of area. The compressor is designed to work with commercial low-power image sensor that outputs image pixels in raster scan fashion, eliminating the need of significant input buffer memory. To demonstrate the advantage, a prototype of the complete WCE system including an FPGA based electronic capsule, a microcontroller based data logger unit and a Windows based image reconstruction software have been developed. The capsule contains the proposed low complexity image compressor and can generate both lossy and lossless compressed bit-stream. The capsule prototype also supports both white light imaging (WLI) and narrow band imaging (NBI) imaging modes and communicates with the data logger in full duplex fashion, which enables configuring the image size and imaging mode in real time during the examination. The developed data logger is portable and has a high data rate wireless connectivity including Bluetooth, graphical display for real time image viewing with state-of-the-art touch screen technology. The data are logged in micro SD cards and can be transferred to PC or Smartphone using card reader, USB interface, or Bluetooth wireless link. The workstation software can decompress and show the reconstructed images. The images can be navigated, marked, zoomed and can be played as video. Finally, ex-vivo testing of the WCE system has been done in pig's intestine to validate its performance

A low complexity image compression algorithm for Bayer color filter array

Digital image in their raw form requires an excessive amount of storage capacity. Image compression is a process of reducing the cost of storage and transmission of image data. The compression algorithm reduces the file size so that it requires less storage or transmission bandwidth. This work presents a new color transformation and compression algorithm for the Bayer color filter array (CFA) images. In a full color image, each pixel contains R, G, and B components. A CFA image contains single channel information in each pixel position, demosaicking is required to construct a full color image. For each pixel, demosaicking constructs the missing two-color information by using information from neighbouring pixels. After demosaicking, each pixel contains R, G, and B information, and a full color image is constructed. Conventional CFA compression occurs after the demosaicking. However, the Bayer CFA image can be compressed before demosaicking which is called compression-first method, and the algorithm proposed in this research follows the compression-first or direct compression method. The compression-first method applies the compression algorithm directly onto the CFA data and shifts demosaicking to the other end of the transmission and storage process. The advantage of the compression-first method is that it requires three time less transmission bandwidth for each pixel than conventional compression. Compression-first method of CFA data produces spatial redundancy, artifacts, and false high frequencies. The process requires a color transformation with less correlation among the color components than that Bayer RGB color space. This work analyzes correlation coefficient, standard deviation, entropy, and intensity range of the Bayer RGB color components. The analysis provides two efficient color transformations in terms of features of color transformation. The proposed color components show lesser correlation coefficient than occurs with the Bayer RGB color components. Color transformations reduce both the spatial and spectral redundancies of the Bayer CFA image. After color transformation, the components are independently encoded using differential pulse-code modulation (DPCM) in raster order fashion. The residue error of DPCM is mapped to a positive integer for the adaptive Golomb rice code. The compression algorithm includes both the adaptive Golomb rice and Unary coding to generate bit stream. Extensive simulation analysis is performed on both simulated CFA and real CFA datasets. This analysis is extended for the WCE (wireless capsule endoscopic) images. The compression algorithm is also realized with a simulated WCE CFA dataset. The results show that the proposed algorithm requires less bits per pixel than the conventional CFA compression. The algorithm also outperforms recent works on CFA compression algorithms for both real and simulated CFA datasets

GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks

In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled data is not abundant. On the other hand, unsupervised deep learning approaches for localization and mapping in unknown environments from unlabelled data have received comparatively less attention in VO research. In this study, we propose a generative unsupervised learning framework that predicts 6-DoF pose camera motion and monocular depth map of the scene from unlabelled RGB image sequences, using deep convolutional Generative Adversarial Networks (GANs). We create a supervisory signal by warping view sequences and assigning the re-projection minimization to the objective loss function that is adopted in multi-view pose estimation and single-view depth generation network. Detailed quantitative and qualitative evaluations of the proposed framework on the KITTI and Cityscapes datasets show that the proposed method outperforms both existing traditional and unsupervised deep VO methods providing better results for both pose estimation and depth recovery.Comment: ICRA 2019 - accepte

Hybrid DWT-DCT algorithm for image and video compression applications

Digital image and video in their raw form require an enormous amount of storage capacity. Considering the important role played by digital imaging and video, it is necessary to develop a system that produces high degree of compression while preserving critical image/video information. There are various transformation techniques used for data compression. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the most commonly used transformation. DCT has high energy compaction property and requires less computational resources. On the other hand, DWT is multiresolution transformation. In this work, we propose a hybrid DWT-DCT algorithm for image compression and reconstruction taking benefit from the advantages of both algorithms. The algorithm performs the Discrete Cosine Transform (DCT) on the Discrete Wavelet Transform (DWT) coefficients. Simulations have been conducted on several natural, benchmark, medical and endoscopic images. Several QCIF, high definition, and endoscopic videos have also been used to demonstrate the advantage of the proposed scheme. The simulation results show that the proposed hybrid DWT-DCT algorithm performs much better than the standalone JPEG-based DCT, DWT, and WHT algorithms in terms of peak signal to noise ratio (PSNR), as well as visual perception at higher compression ratio. The new scheme reduces “false contouring” and “blocking artifacts” significantly. The rate distortion analysis shows that for a fixed level of distortion, the number of bits required to transmit the hybrid coefficients would be less than those required for other schemes Furthermore, the proposed algorithm is also compared with the some existing hybrid algorithms. The comparison results show that, the proposed hybrid algorithm has better performance and reconstruction quality. The proposed scheme is intended to be used as the image/video compressor engine in imaging and video applications

Efficient data encoder for endoscopic imaging applications

The invention of medical imaging technology revolved the process of diagnosing diseases and opened a new world for better studying inside of the human body. In order to capture images from different human organs, different devices have been developed. Gastro-Endoscopy is an example of a medical imaging device which captures images from human gastrointestinal. With the advancement of technology, the issues regarding such devices started to get rectified. For example, with the invention of swallow-able pill photographer which is called Wireless Capsule Endoscopy (WCE); pain, time, and bleeding risk for patients are radically decreased. The development of such technologies and devices has been increased and the demands for instruments providing better performance are grown along the time. In case ofWCE, the special feature requirements such as a small size (as small as an ordinary pill) and wireless transmission of the captured images dictate restrictions in power consumption and area usage. In this research, the reduction of image encoder hardware cost for endoscopic imaging application has been focused. Several encoding algorithms have been studied and the comparative results are discussed. An efficient data encoder based on Lempel-Ziv-Welch (LZW) algorithm is presented. The encoder is a library-based one where the size of library can be modified by the user, and hence, the output data rate can be controlled according to the bandwidth requirement. The simulation is carried out with several endoscopic images and the results show that a minimum compression ratio of 92.5 % can be achieved with a minimum reconstruction quality of 30 dB. The hardware architecture and implementation result in Field-Programmable Gate Array (FPGA) for the proposed window-based LZW are also presented. A new lossy LZW algorithm is proposed and implemented in FPGA which provides promising results for such an application