Joan Bartrina-Rapesta

Lossless compression of medical images based on HEVC intra coding

The recent introduction of the High Efficiency Video Coding (HEVC) standard provides opportunities to improve medical image compression in picture archiving and communications systems. In this paper, we propose improvements to the HEVC intra coding process for lossless compression of grayscale anatomical medical images, which are characterized by their large amount of edges. Specifically, we propose alternative angular and planar prediction modes that are based on sample-wise differential pulse code modulation (DPCM) with an increased range of directionalities. We also propose an implementation of the DPCM decoding process that maintains the block-wise coding structure of HEVC. Evaluation results on various medical images show that the proposed DPCM modes efficiently predict the large amount of edges in these images achieving bit-rate savings of up to 15%.

JPEG2000 ROI coding through component priority for digital mammography

Region Of Interest (ROI) coding is a prominent feature of some image coding systems aimed to prioritize specific areas of the image through the construction of a codestream that, decoded at increasing bit-rates, recovers the ROI first and with higher quality than the rest of the image. JPEG2000 is a wavelet-based coding system that is supported in the Digital Imaging and Communications in Medicine (DICOM) standard. Among other features, JPEG2000 provides lossy-to-lossless compression and ROI coding, which are especially relevant to the medical community. But, due to JPEG2000 supported ROI coding methods that guarantee lossless coding are not designed to achieve a high degree of accuracy to prioritize ROIs, they have not been incorporated in the medical community. This paper introduces a ROI coding method that is able to prioritize multiple ROIs at different priorities, guaranteeing lossy-to-lossless coding. The proposed ROI Coding Through Component Prioritization (ROITCOP) method uses techniques of rate-distortion optimization combined with a simple yet effective strategy of ROI allocation that employs the multi-component support of JPEG2000 codestream. The main insight in ROITCOP is the allocation of each ROI to an component. Experimental results indicate that this ROI allocation strategy does not penalize coding performance whilst achieving an unprecedented degree of accuracy to delimit ROIs. The proposed ROITCOP method maintains JPEG2000 compliance, thus easing its use in medical centers to share images. This paper analyzes in detail the use of ROITCOP to mammographies, where the ROIs are identified by computer-aided diagnosis. Extensive experimental tests using various ROI coding methods suggest that ROITCOP achieves enhanced coding performance.

Divide-and-Conquer Strategies for Hyperspectral Image Processing: A Review of Their Benefits and Advantages

In the field of geophysics, huge volumes of information often need to be processed with complex and time-consuming algorithms to better understand the nature of the data at hand. A particularly useful instrument within a geophysicists toolbox is a set of decorrelating transforms. Such transforms play a key role in the acquisition and processing of satellite-gathered information, and notably in the processing of hyperspectral images. Satellite images have a substantial amount of redundancy that not only renders the true nature of certain events less perceivable to geophysicists but also poses an issue to satellite makers, who have to exploit this data redundancy in the design of compression algorithms due to the constraints of down-link channels. This issue is magnified for hyperspectral imaging sensors, which capture hundreds of visual representations of a given targeteach representation (called a component or a band) for a small range of the light spectrum. Although seldom alone, decorrelation transforms are often used to alleviate this situation by changing the original data space into a representation where redundancy is decreased and valuable information is more apparent.

JPEG2000 Encoding of Remote Sensing Multispectral Images With No-Data Regions

Most sensors used for remote sensing (RS) purposes capture more than one component to seize different features from the Earths surface. Usually, either multispectral images acquired for RS applications are corrected or the user/application determines valid regions within the image. Consequently, regions without information may emerge ( no-data regions). This letter proposes to encode multispectral images with no-data regions through the JPEG2000 framework, taking into account the lack of importance of these irrelevant regions. Experimental results, performed on data from real scenarios, suggest that the best approach analyzed is the shape-adaptive (SA) Karhunen-Loe¿ve transform to decorrelate the spectral redundancy and then the SA multicomponent JPEG2000. The coding-performance improvement over other coding systems considered (Binary Set Splitting with K-D Trees, SA Wavelet Difference Reduction, and SA TARP) is from 5 to 20 dB in signal-to-noise ratio energy.

Lossy-to-lossless 3D image coding through prior coefficient lookup tables

This paper describes a low-complexity, high-efficiency, lossy-to-lossless 3D image coding system. The proposed system is based on a novel probability model for the symbols that are emitted by bitplane coding engines. This probability model uses partially reconstructed coefficients from previous components together with a mathematical framework that captures the statistical behavior of the image. An important aspect of this mathematical framework is its generality, which makes the proposed scheme suitable for different types of 3D images. The main advantages of the proposed scheme are competitive coding performance, low computational load, very low memory requirements, straightforward implementation, and simple adaptation to most sensors.

HEVC-based lossless compression of Whole Slide pathology images

This paper proposes an HEVC-based method for lossless compression of Whole Slide pathology Images (WSIs). Based on the observation that WSIs usually feature a high number of edges and multidirectional patterns due to the great variety of cellular structures and tissues depicted, we combine the advantages of sample-by-sample differential pulse code modulation (SbS-DPCM) and edge prediction into the intra coding process. The objective is to enhance the prediction performance where strong edge information is encountered. This paper also proposes an implementation of the decoding process that maintains the block-wise coding structure of HEVC when SbS-DPCM and edge prediction are employed. Experimental results on various WSIs show that the proposed method attains average bit-rate savings of 7.67%.

JPEG2000 ROI Coding With Fine-Grain Accuracy Through Rate-Distortion Optimization Techniques

Region of interest (ROI) coding is a feature of prominent image coding systems that enables the specification of different coding priorities to certain regions of the image. JPEG2000 provides ROI coding through two mechanisms: either modifying wavelet coefficients or using rate-distortion optimization techniques. Although ROI coding methods based on the modification of wavelet coefficients provide an excellent accuracy to delimit the ROI area (referred to as fine-grain accuracy), they significantly penalize the coding efficiency. On the other hand, methods based on rate-distortion optimization improve the coding efficiency but, so far, have not been able to achieve the intended fine-grain accuracy. This letter introduces two ROI coding methods that, using rate-distortion optimization techniques, achieve a fine-grain accuracy, comparable to the one obtained when wavelet coefficients are modified, and are competitive in terms of coding efficiency.

JPEG2000 Arbitrary ROI Coding through Rate-Distortion Optimization Techniques

Region of interest (ROI) coding is a mechanism deployed in several image coding systems to enable different degrees of coding priority to specific regions of the image. JPEG2000 standard provides two ROI coding methods. However, both of them are based in mechanisms that scale the quantized coefficients. This compels to encode the additional bit-planes needed for the scaling, causing a penalization in the overall coding performance, and the ROIs can not be modified without a complete re-encoding of the image. This paper introduces two ROI coding methods that use rate-distortion optimization techniques to prioritize arbitrary ROIs over the rest of the image without penalizing the overall coding performance. Experimental results suggest that the proposed methods achieve a close to optimal accuracy, improving the Implicit ROI coding method in terms of ROI rate distortion performance.

JPEG2000 ROI coding method with perfect fine-grain accuracy and lossless recovery

Medical images have high spatial and high bit-depth resolution (12 bits per sample or more). These high resolutions allow computer-aided diagnosis, which are exploited by radiologists to identify relevant medical areas, known as Regions of Interest (ROI). In image compression, ROI coding allows to recover the ROI earlier than the rest of the image. In JPEG2000, ROI coding may be provided through two different mechanisms: either by modifying wavelet coefficients, or by rate-distortion optimization techniques. The former obtains an excellent accuracy to delimit ROIs, but, in some cases, the ROI and the background can not be encoded losslessly; the latter is usually not able to achieve the intended fine-grain accuracy, but it overcomes the lossless encoding shortcoming. This article introduces a ROI coding method based on rate-distortion optimization techniques that recovers the ROI and the background losslessly, regardless of the high bit-depth resolution, and that yields an accuracy equivalent to MaxShift and Scaling, the two compliant JPEG2000 ROI coding methods based on modifying wavelet coefficients. The proposed method is JPEG2000 compliant.

Rate control for lossless region of interest coding in HEVC intra-coding with applications to digital pathology images

This paper proposes a rate control algorithm for lossless region of interest (RoI) coding in HEVC intra-coding. The algorithm is developed for digital pathology images and allows for random access to the data. Based on an input RoI mask, the algorithm first encodes the RoI losslessly. According to the bit rate spent on the RoI, it then encodes the background by using rate control in order to meet an overall target bit rate. In order to increase rate control accuracy, the algorithm uses an R-λ model to approximate the slope of the rate-distortion curve, and updates any related model parameters during the encoding process. Random access is attained by coding the data using independent tiles. Experimental results show that the proposed algorithm attains the overall bit rate very accurately while providing lossless reconstruction of the RoI.